Methods in
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Jean-Paul Borg Editor
PDZ Mediated
Interactions
Methods and Protocols
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PDZ Mediated Interactions
Methods and Protocols
Edited by
Jean-Paul Borg
Centre de Recherche en Cancérologie de Marseille, Aix-Marseille University, Inserm, CNRS,
Institut Paoli-Calmettes, Marseille, France; Institut Universitaire de France (IUF), Paris, France
Editor
Jean-Paul Borg
Centre de Recherche en Cancérologie
de Marseille
Aix-Marseille University, Inserm, CNRS, Institut Paoli-Calmettes
Marseille, France
Institut Universitaire de France (IUF)
Paris, France
ISSN 1064-3745
ISSN 1940-6029 (electronic)
Methods in Molecular Biology
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Preface
All biological functions are regulated by protein networks whose organization relies on
finely tuned protein-protein and protein-lipid interactions. PDZ domains represent one of
the most widely distributed protein-protein interaction domains and contribute to a large
number of biological processes, from the plasma membrane to the nucleus, especially in cellcell communication and cell polarity. Their importance in physiology and pathologies such
as cancer, neurodegenerative and infectious diseases being now well established since their
discovery in the 1990s, they have brought the interest of many laboratories, which has led to
the development of dedicated techniques able to predict and identify their ligands, characterize their functions in normal and pathological conditions, and, more recently, conduct
the design of peptide or chemical inhibitors. This volume provides a comprehensive overview of the techniques currently applied to identify and characterize PDZ-mediated interactions and opens the discussion on priority topics emerging in this area of investigation
(promiscuity, multimodularity, regulation, and viral recognition by PDZ domains).
Marseille, France
Jean-Paul Borg
v
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
v
ix
1 Identification of PDZ Interactions by Yeast Two-Hybrid Technique . . . . . . . . . .
Monica Castro-Cruz, Marta Monserrat-Gomez, Jean-Paul Borg,
Pascale Zimmermann, and Eric Bailly
2 Identification of PDZ Interactions by Affinity Purification
and Mass Spectrometry Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Avais M. Daulat, Stéphane Audebert, Mônica Wagner,
Luc Camoin, and Jean-Paul Borg
3 Identification of PDZ Interactions by Proteomic Peptide Phage Display . . . . . . .
Susanne Lüchow, Gustav N. Sundell, and Ylva Ivarsson
4 A Computational Protocol to Analyze PDZ/PBM Affinity
Data Obtained by High-Throughput Holdup Assay. . . . . . . . . . . . . . . . . . . . . . . . .
Pau Jané, Lionel Chiron, Goran Bich, Gilles Travé,
and Yves Nominé
5 Study of PDZ–Peptide and PDZ–Lipid Interactions by Surface
Plasmon Resonance/BIAcore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pascale Zimmermann and Antonio Luis Egea-Jimenez
6 PDZ Sample Quality Assessment by Biochemical and Biophysical
Characterizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Célia Caillet-Saguy, Sébastien Brûlé, Nicolas Wolff,
and Bertrand Raynal
7 Crystallographic Studies of PDZ Domain–Peptide Interactions
of the Scribble Polarity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Janesha C. Maddumage, Bryce Z. Stewart, Patrick O. Humbert,
and Marc Kvansakul
8 A Fluorescence-Based Assay to Determine PDZ–Ligand Binding
Thermodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Young Joo Sun and Ernesto J. Fuentes
9 Unveiling the Folding Mechanism of PDZ Domains . . . . . . . . . . . . . . . . . . . . . . . .
Candice Gautier and Stefano Gianni
10 Development of Peptide-Based PDZ Domain Inhibitors. . . . . . . . . . . . . . . . . . . . .
Dominik J. Essig, Javier R. Balboa, and Kristian Strømgaard
11 Dynamic Control of Signaling by Phosphorylation
of PDZ Binding Motifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Márton A. Simon and Lászlo Nyitray
12 Chemical Synthesis of PDZ Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Christin Kossmann, Sana Ma, Louise S. Clemmensen,
and Kristian Strømgaard
1
vii
17
41
61
75
89
125
137
149
157
179
193
viii
13
14
15
16
Contents
Viral PDZ Binding Motifs Influence Cell Behavior Through
the Interaction with Cellular Proteins Containing PDZ Domains . . . . . . . . . . . . .
Carlos Castaño-Rodriguez, Jose M. Honrubia,
Javier Gutiérrez-Álvarez, Isabel Sola, and Luis Enjuanes
Computational Design of PDZ-Peptide Binding. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nicolas Panel, Francesco Villa, Vaitea Opuu, David Mignon,
and Thomas Simonson
Mechanoregulation of PDZ Proteins, An Emerging Function . . . . . . . . . . . . . . . .
Elsa Bazellières and André Le Bivic
Rational Design of PDZ Domain Inhibitors: Discovery
of Small Organic Compounds Targeting PDZ Domains . . . . . . . . . . . . . . . . . . . . .
Laurent Hoffer, Philippe Roche, and Xavier Morelli
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
217
237
257
277
291
Contributors
STÉPHANE AUDEBERT • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes,
CRCM, Marseille Protéomique, Marseille, France
ERIC BAILLY • Centre de Recherche en Cancérologie de Marseille (CRCM), Aix-Marseille
Université, Inserm, CNRS, Institut Paoli-Calmettes, Marseille, France
JAVIER R. BALBOA • Department of Drug Design and Pharmacology, Center for
Biopharmaceuticals, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk A/
S, Research Chemistry 3, Måløv, Denmark
ELSA BAZELLIÈRES • Aix Marseille Université, CNRS, IBDM–UMR7288, Turing Centre for
Living Systems, Marseille, France
GORAN BICH • (Equipe labelisée Ligue, 2015) Institut de Génétique et de Biologie Moléculaire
et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg,
Illkirch, France
JEAN-PAUL BORG • Centre de Recherche en Cancérologie de Marseille, Aix-Marseille
University, Inserm, CNRS, Institut Paoli-Calmettes, Marseille, France; Institut
Universitaire de France (IUF), Paris, France
SÉBASTIEN BRÛLÉ • Institut Pasteur, Plate-forme de Biophysique Moléculaire, CNRS UMR
3528, Paris, France
CÉLIA CAILLET-SAGUY • Institut Pasteur, Unité Récepteurs-Canaux, CNRS UMR 3571,
Paris, France
LUC CAMOIN • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM,
Marseille Protéomique, Marseille, France
CARLOS CASTAÑO-RODRIGUEZ • Department of Molecular and Cell Biology, Centro Nacional
de Biotecnologı́a (CNB-CSIC), Madrid, Spain
MONICA CASTRO-CRUZ • Centre de Recherche en Cancerologie de Marseille (CRCM), Equipe
Zimmermann labellisée Ligue 2018 – 2019, Aix-Marseille Université, Inserm, CNRS,
Institut Paoli-Calmettes, Marseille, France; Department of Human Genetics, K. U.
Leuven, Leuven, Belgium
LIONEL CHIRON • CASC4DE, Strasbourg, France
LOUISE S. CLEMMENSEN • Department of Drug Design and Pharmacology, Center for
Biopharmaceuticals, University of Copenhagen, Copenhagen, Denmark
AVAIS M. DAULAT • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes,
CRCM, Equipe labellisée Ligue ‘Cell polarity, cell signaling and cancer’, Marseille, France
ANTONIO LUIS EGEA-JIMENEZ • Centre de Recherche en Cancérologie de Marseille (CRCM),
Equipe Zimmermann labellisée Ligue 2018, Aix-Marseille Université, Inserm, CNRS and
Institut Paoli-Calmettes, Marseille, France
LUIS ENJUANES • Department of Molecular and Cell Biology, Centro Nacional de
Biotecnologı́a (CNB-CSIC), Madrid, Spain
DOMINIK J. ESSIG • Department of Drug Design and Pharmacology, Center for
Biopharmaceuticals, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk A/
S, Research Chemistry 3, Måløv, Denmark
ERNESTO J. FUENTES • Department of Biochemistry, University of Iowa, Iowa City, IA, USA;
Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
ix
x
Contributors
CANDICE GAUTIER • Istituto Pasteur-Fondazione Cenci Bolognetti and Istituto di Biologia
e Patologia Molecolari del CNR, Dipartimento di Scienze Biochimiche “A. Rossi Fanelli”,
` di Roma, Rome, Italy
Sapienza Universita
STEFANO GIANNI • Istituto Pasteur-Fondazione Cenci Bolognetti and Istituto di Biologia
e Patologia Molecolari del CNR, Dipartimento di Scienze Biochimiche “A. Rossi Fanelli”,
` di Roma, Rome, Italy
Sapienza Universita
JAVIER GUTIÉRREZ-ÁLVAREZ • Department of Molecular and Cell Biology, Centro Nacional
de Biotecnologı́a (CNB-CSIC), Madrid, Spain
LAURENT HOFFER • Centre de Recherche en Cancérologie de Marseille (CRCM), AixMarseille Université, Inserm, CNRS and Institut Paoli-Calmettes, Marseille, France
JOSE M. HONRUBIA • Department of Molecular and Cell Biology, Centro Nacional de
Biotecnologı́a (CNB-CSIC), Madrid, Spain
PATRICK O. HUMBERT • Department of Biochemistry and Genetics, La Trobe University,
Bundoora, VIC, Australia; La Trobe Institute for Molecular Science, La Trobe University,
Bundoora, VIC, Australia
YLVA IVARSSON • Department of Chemistry, BMC, Uppsala University, Uppsala, Sweden
PAU JANÉ • (Equipe labelisée Ligue, 2015) Institut de Génétique et de Biologie Moléculaire et
Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg,
Illkirch, France
CHRISTIN KOSSMANN • Department of Drug Design and Pharmacology, Center for
Biopharmaceuticals, University of Copenhagen, Copenhagen, Denmark
MARC KVANSAKUL • Department of Biochemistry and Genetics, La Trobe University,
Bundoora, VIC, Australia; La Trobe Institute for Molecular Science, La Trobe University,
Bundoora, VIC, Australia
ANDRÉ LE BIVIC • Aix Marseille Université, CNRS, IBDM–UMR7288, Turing Centre for
Living Systems, Marseille, France
SUSANNE LÜCHOW • Department of Chemistry, BMC, Uppsala University, Uppsala, Sweden
SANA MA • Department of Drug Design and Pharmacology, Center for Biopharmaceuticals,
University of Copenhagen, Copenhagen, Denmark
JANESHA C. MADDUMAGE • Department of Biochemistry and Genetics, La Trobe University,
Bundoora, VIC, Australia; La Trobe Institute for Molecular Science, La Trobe University,
Bundoora, VIC, Australia
DAVID MIGNON • Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654),
Ecole Polytechnique, Palaiseau, France
MARTA MONSERRAT-GOMEZ • Centre de Recherche en Cancérologie de Marseille (CRCM),
JPB team is Equipe labellisée Ligue 2018 – 2019, Aix-Marseille Université, Inserm, CNRS,
Institut Paoli-Calmettes, Marseille, France
XAVIER MORELLI • Centre de Recherche en Cancérologie de Marseille (CRCM), Aix-Marseille
Université, Inserm, CNRS and Institut Paoli-Calmettes, Marseille, France
YVES NOMINÉ • (Equipe labelisée Ligue, 2015) Institut de Génétique et de Biologie Molé
culaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de
Strasbourg, Illkirch, France
LÁSZLÓ NYITRAY • Department of Biochemistry, ELTE Eötvös Loránd University,
Budapest, Hungary
VAITEA OPUU • Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole
Polytechnique, Palaiseau, France
NICOLAS PANEL • Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654),
Ecole Polytechnique, Palaiseau, France
Contributors
xi
BERTRAND RAYNAL • Institut Pasteur, Plate-forme de Biophysique Moléculaire, CNRS UMR
3528, Paris, France
PHILIPPE ROCHE • Centre de Recherche en Cancérologie de Marseille (CRCM), Aix-Marseille
Université, Inserm, CNRS and Institut Paoli-Calmettes, Marseille, France
MÁRTON A. SIMON • Department of Biochemistry, ELTE Eötvös Loránd University,
Budapest, Hungary
THOMAS SIMONSON • Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654),
Ecole Polytechnique, Palaiseau, France
ISABEL SOLA • Department of Molecular and Cell Biology, Centro Nacional de Biotecnologı́a
(CNB-CSIC), Madrid, Spain
BRYCE Z. STEWART • Department of Biochemistry and Genetics, La Trobe University,
Bundoora, VIC, Australia; La Trobe Institute for Molecular Science, La Trobe University,
Bundoora, VIC, Australia
KRISTIAN STRØMGAARD • Department of Drug Design and Pharmacology, Center for
Biopharmaceuticals, University of Copenhagen, Copenhagen, Denmark
YOUNG JOO SUN • Department of Biochemistry, University of Iowa, Iowa City, IA, USA
GUSTAV N. SUNDELL • Department of Chemistry, BMC, Uppsala University, Uppsala,
Sweden; Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech
University, Shanghai, China
GILLES TRAVÉ • (Equipe labelisée Ligue, 2015) Institut de Génétique et de Biologie Molé
culaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de
Strasbourg, Illkirch, France
FRANCESCO VILLA • Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654),
Ecole Polytechnique, Palaiseau, France
MÔNICA WAGNER • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes,
CRCM, Marseille Protéomique, Marseille, France
NICOLAS WOLFF • Institut Pasteur, Unité Récepteurs-Canaux, CNRS UMR 3571,
Paris, France
PASCALE ZIMMERMANN • Centre de Recherche en Cancerologie de Marseille (CRCM), Equipe
Zimmermann labellisée Ligue 2018 – 2019, Aix-Marseille Université, Inserm, CNRS,
Institut Paoli-Calmettes, Marseille, France; Department of Human Genetics, K. U.
Leuven, Leuven, Belgium
Chapter 1
Identification of PDZ Interactions by Yeast Two-Hybrid
Technique
Monica Castro-Cruz, Marta Monserrat-Gomez, Jean-Paul Borg,
Pascale Zimmermann, and Eric Bailly
Abstract
The yeast two-hybrid technique is a powerful method to detect direct protein–protein interactions. Due to
its accessibility, speed, and versatility, this technique is easy to set up in any laboratory and suitable for small
and large scale screenings. Here we describe the implementation of an array-based screening that allows for
the probing of the entire human PDZ ORFeome (or hPDZome) by yeast two-hybrid technique. With this
approach, one can rapidly identify the PDZ domains that are able to interact (up to KD in the high μmolar
range) with any candidate protein among a panel of 266 individual clones, thereby comprehensively
identifying its PDZ interactome.
Key words Yeast two-hybrid, Two-hybrid array, Protein–protein interaction, GAL4, Yeast strain,
PDZ interactions
1
Introduction
The yeast two-hybrid (Y2H) method was developed in the late
1980s and remains a straightforward genetic approach to detect
direct protein–protein interactions (PPIs) [1, 2]. It takes advantage
of the modular nature of transcription factors (TFs) like GAL4 to
render the expression of reporter genes dependent on the binding
properties of two partner proteins (Fig. 1). GAL4 comprises two
functionally independent domains, a DNA binding domain
(GAL4-BD) and a transcription activation domain (GAL4-AD)
that can be separately fused to the proteins of interest. If the
resulting hybrid components can interact through the two proteins
of interest, then a functional GAL4 is reconstituted allowing for the
transcription of the reporter/survival genes. In this setting, GAL4BD is commonly appended to one partner that serves as “bait” (X),
Monica Castro-Cruz and Marta Monserrat-Gomez contributed equally to this work.
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021
1
2
Monica Castro-Cruz et al.
Fig. 1 Principle/Overview of the yeast two-hybrid technique. (a) The protein of
interest (X), corresponding to the bait is fused to the DNA binding domain of
GAL4 (GAL4-BD-X), while the putative binder (Y) is fused to the activation domain
(GAL4-AD-Y). (b) GAL4-BD-X binds the upstream activator sequence (UAS) of the
promoter. Upon interaction between the bait and the prey, a functional GAL4 is
reconstituted, thereby promoting the recruitment of RNA polymerase II and the
transcription of the reporter gene
whereas Gal4-AD is fused to potential interactors called “prey”
(Y) (Fig. 1). The bait and prey constructs are separately expressed
in haploid yeast strains of opposite mating type (MATa and MATα).
The auxotrophic markers HIS3 and URA3 together with the lacZ
gene are the three most frequently used reporter/survival genes,
the latter enabling a colorimetric readout of the tested PPI [3, 4].
Even though the Y2H approach is a powerful screening
approach, it should be kept in mind that it is prone to false-positive
and false-negative results [5]. False positives may result from spurious activation of the reporter gene in the absence of an interacting
partner. False negatives, on the other hand, may stem from the
requirement of posttranslational modifications, potential steric
constrains imposed by the cloning or from the failure of the interacting partners to reach the nuclear compartment. It is always
recommended to add negative and positive controls in addition to
the proteins of interest. It is also crucial to cross-validate the results
of a Y2H screen by alternative approaches (coimmunoprecipitation,
mass spectrometry, and surface plasmon resonance, among others).
Y2H Screening of PDZ Interactions
3
Since its development, the Y2H approach has now been applied
to an impressive variety of organisms and in various scale settings,
from pairwise to genomic level screens [4]. In particular, Y2H data
obtained with random cDNA libraries have opened the way to the
establishment of highly elaborate interactome maps within the
proteome of numerous species. However, in recent years the use
of smaller and arrayed libraries has been increasingly exploited.
These arrayed Y2H libraries can assemble proteins or domains
belonging to the same structural family or to diverse subcellular
entities. Their use can lead to a more comprehensive picture of the
interactions mediated by the array members [6–9]. Moreover, a
major advantage of arrays over random cDNA libraries when
screening by Y2H is their suitability for high-throughput technologies, making their implementation faster, more accessible, and
comprehensive.
Here, we describe the implementation of a Y2H screen that
focuses on PDZ-mediated interactions using an array-based human
PDZ ORFeome (thereafter named PDZome) generated in our
laboratory [10]. The PDZome holds 266 PDZ domains based on
the prediction and manual annotation of all the PDZ sequences.
New boundaries at the N- and C-termini have been established,
contributing to the folding, solubility, and affinity of the PDZ
domains [11]. As depicted in Fig. 2, candidate proteins known or
suspected to contain PDZ-binding motifs (PBMs) are used as baits
to screen the individual Y2H prey clones of the hPDZome array.
2
Materials
All solutions should be prepared using ultrapure water and autoclaved or sterile filtered. Store all reagents at room temperature
(unless indicated otherwise).
2.1 Design
of Primers for Bait
Constructs
The choice of a bait sequence to screen the PDZome library may be
guided by preliminary evidence of PDZ binding properties of a
candidate protein. When no information is available and because
most PDZ domains interact with the C-terminal end of their protein targets, a standard approach is to fuse the last fifteen residues of
a candidate protein in frame with the C-terminus of the Gal4-BD
moiety. In this case, generating the same bait but truncated of its
last three amino acids provides a convenient negative control as this
truncation is known to disrupt most PBM/PDZ interactions [12].
Of note, the use of larger cytosolic fragments may also prove
relevant when suspecting the occurrence of an internal noncanonical PBM within a given PDZ binding protein.
Below are guidelines for the design of primers for the cloning
of bait constructs using the Gateway® cloning system. This cloning
strategy first requires the addition of attB1 and attB2
4
Monica Castro-Cruz et al.
Fig. 2 Construction of the hPDZome array for yeast two-hybrid screening. (a) The human PDZome was built
using the Gateway ® system. The 266 entry clones in the pZeo vector were provided in 96-well plates by
NZYTech. Each entry clone corresponds to one ORF of the hPDZome. (b) The 266 PDZ ORFs were subcloned
into the pACT2-AD vector using Gateway ® LR clonase and verified by sequencing. (c) Once validated, each
pACT2-AD PDZ prey was transformed into the Y187 prey yeast strain and transformants were selected by
plating on synthetic complete agar plates without leucine (SC agar -Leu). The final array ready for mating
consists of three 96-well plates with yeast clones expressing the hPDZome fused to the Gal4 activation
domain (AD)
recombination sites flanking the target gene sequence. Introduction of these sites is easily achieved by PCR using oligonucleotides
designed as follows.
1. Design a For-attB1-ORF forward primer based on the
sequence 50 -GGGG ACA AGT TTG TAC AAA AAA GCA
GGC TNN NNN-30 where the attB1 recombination site is
underlined and N letters correspond to the 50 -end of your
Y2H Screening of PDZ Interactions
5
bait gene. The codon sequence shown here within the attB1
site is in frame with the coding sequence of the upstream
GAL4BD region. It is therefore critical to make sure that the
sequence of the bait is in frame with the indicated attB1 reading
sequence as it is the case here with the TNN fusion codon.
2. Design a Rev-attB2-ORFwt reverse primer based on the
sequence 50 -GGGG AC CAC TTT GTA CAA GAA AGC
TGG GTC TTA NNN-30 where the attB2 recombination site
is underlined and N letters correspond to the reverse and
complementary 30 -end wild-type sequence of the bait construct. Pay attention to the fact that a stop codon has to be
placed between the 30 -end of the target gene sequence and the
attB2 site as additional amino acid residues at the C-terminus
of the bait are likely to disrupt its PBM properties. In the above
Rev-attB2-ORFwt sequence, a TAA stop codon (denoted in
bold letters) has been introduced but the endogenous stop
codon of the bait target can be used as well.
3. Following the same guidelines as described in item 2, design a
second Rev-attB2-ORFmut reverse primer in which the
sequence of the last three C-terminal codons of the bait is
lacking. This primer will serve for the cloning of the PBM
mutant bait construct.
2.2 PCR
Amplification
and Purification
of the Bait Constructs
1. 5 mM dNTPs.
2. 10 μM forward and reverse primers.
3. Bait DNA to be used as a template for the PCR reaction.
4. High-fidelity DNA polymerase.
5. Concentrated stocks of reaction buffer and magnesium chloride provided with the DNA polymerase.
6. TAE buffer: 40 mM Tris, 40 mM acetate, 1 mM EDTA,
pH 8.3.
7. 1% or 2% agarose gels prepared in TAE buffer.
8. Gel DNA extraction kit of your choice. Be aware that some kits
may not be optimized for the recovery of small DNA
fragments.
2.3 Cloning
of the Bait Constructs
1. pDONr/pZeo® plasmid DNA.
2. pGBT9-GW plasmid DNA.
3. BP recombinase®.
4. LR recombinase®.
5. TE buffer: 10 mM Tris–HCl, pH 8; and 1 mM EDTA, pH 8.
6. Chemically competent DH5α bacteria.
6
Monica Castro-Cruz et al.
7. LB liquid medium: 10 g/L tryptone, 10 g/L NaCl, 5 g/L
yeast extract.
8. LB solid medium: 10 g/L tryptone, 10 g/L NaCl, 5 g/L yeast
extract, 20 g/L agar.
9. 1000 Zeocin stock solution: 100 mg/mL Zeocin
10. 1000 Ampicillin stock solution: 100 mg/mL Ampicillin
11. DNA miniprep extraction kit.
12. BsrGI restriction enzyme with its reaction buffer.
1. Y187: MATα, ura3-52, his3-200, ade2-101, trp1-901, leu2-3,
112, gal4Δ, met-, gal80Δ, MEL1, URA3:: GAL1UAS -GAL1TATA-lacZ.
2.4 Y2H Yeast
Strains
2. AH109: MATa, trp1-901, leu2-3, 112, ura3-52, his3-200,
gal4Δ,
gal80Δ,
LYS2::
GAL1UAS-GAL1TATA-HIS3,
GAL2UAS-GAL2TATA-ADE2, URA3:: MEL1UAS-MEL1
TATA-lacZ.
1. Yeast extract–peptone–dextrose (YPD) medium: 10 g/L yeast
extract 10 g/L, 20 g/L Bacto peptone 20 g/L, 20 g/L glucose, 100 mg/L adenine hemisulfate.
2.5 Yeast
Culture Media
2. YPD solid medium: YPD medium plus 20 g/L Bacto agar.
3. Amino acid powder mix: Mix 6 g of each of the amino acids
listed in Table 1, supplement the mix with 6 g of adenine
hemisulfate.
4. 125 supplement solutions: 20 mM uracil; 100 mM leucine;
100 mM histidine; 40 mM tryptophan. Histidine and tryptophan 125X solutions should be stored at 4 C and protected
from light
5. Synthetic Complete (SC) liquid Medium: 1.7 g/L yeast nitrogen base without amino acids, 5 g/L ammonium sulfate, 20 g/
L glucose, 1.3 g/L amino acid powder mix. Add 8 mL per liter
of the required 125 supplement solutions to achieve the
desired selection condition as detailed in Table 2.
6. SC solid medium: SC medium plus 20 g/L Bacto agar.
Table 1
Amino acid list to supplement synthetic complete media
Alanine
Asparagine
Glutamine
Lysine
Proline
Tyrosine
Arginine
Cysteine
Glycine
Methionine
Serine
Valine
Aspartic acid
Glutamic acid
Isoleucine
Phenylalanine
Threonine
Y2H Screening of PDZ Interactions
7
Table 2
Amino acid dropout supplements and selection purposes
Selection purpose
Selective SC medium
Required supplement (8 mL/L)
Prey selection
SC -Leu
Trp, His, Ura
Bait selection
SC -Trp
Leu, His, Ura
Bait autoactivation readout
SC -Trp -His
Leu, Ura
Mating efficiency readout
-Leu -Trp
His, Ura
Y2H interaction readout
-Leu -Trp -His
Ura
2.6 Yeast
Transformation
1. Wild-type and PBM mutant Y2H bait vectors (pGBT9-GW).
2. YPD liquid medium.
3. Sterile ddH2O.
4. SC –Trp plates (see Table 2).
5. 10 TE buffer: 100 mM Tris–HCl, pH 8; and 10 mM EDTA,
pH 8 (autoclave).
6. 10 lithium acetate (LiAc): 1 M LiAc. Adjust pH to 7.5 with
dilute acetic acid, filter-sterilize on 0.22 μm membrane, and
store at 4 C.
7. 10 mg/mL single-stranded carrier DNA from salmon sperm.
8. PEG–TE–LiAC solution: 50% (w/v) polyethylene glycol 4000
(PEG 4000) in 1 TE–LiAc solution (autoclave).
9. Dimethyl sulfoxide (DMSO).
2.7 Screening
of the Human PDZome
Library
1. SC -Leu plates.
2. Y2H PDZome array (frozen stock).
3. Wild-type and PBM mutant bait-containing AH109 strains.
4. SC -Leu liquid medium.
5. SC -Trp liquid medium.
6. YPD liquid medium.
7. SC -Leu, -Trp plates.
8. SC -Leu, -Trp, -His plates.
9. 96w polypropylene deep-well plates
10. 96-well U-bottom microplates.
8
3
Monica Castro-Cruz et al.
Methods
3.1 Amplification
and Purification
of the Y2H Bait
Constructs
1. Set up two 50 μL PCR reactions to amplify the wild type and
PBM mutant versions of the bait of interest using the forward
and reverse primers described in Subheading 2.1.
2. Start by mixing 1 μL of dNTPs, 2 μL of Forward and Reverse
primers, 50–100 ng of template bait DNA, the volume of PCR
reaction buffer and magnesium chloride recommended by the
DNA polymerase manufacturer, complete with sterile ddH2O
water up to 50 μL and finish with the addition of DNA polymerase, as recommended by the manufacturer.
3. Run both PCR reactions according to the DNA polymerase
manufacturer recommendations. A rather low number (typically 18 to 20) of PCR cycles is generally enough when amplifying short DNA fragments.
4. Check the size and abundance of the PCR product by electrophoresis, using a 1–2% agarose gel (depending on the size of
the amplified DNA fragment) and loading 2 μL of the PCR
reaction.
5. Gel-purify 10–20 μL of the PCR products on a new agarose gel
to remove any unreacted primers that may interfere with the
recombination step. To accomplish this step use the reagents
provided with the gel DNA extraction kit and following the
manufacturer instructions. Be aware that small PCR products
(<100 bp) tend not to bind thoroughly to the column, thereby
potentially reducing their recovery after purification.
3.2 Cloning of Bait
Entry Clones in pDONr/
pZeo®
Here we describe the successive steps for subcloning PCR amplified
bait constructs, as obtained in Subheading 3.1, into the entry clone
vector pDONr/pZEO®
1. Set up two BP recombination reactions by combining 1–2 μL
of gel-purified PCR fragment, 300 ng of pDONr/pZeo®, 2 μL
of TE buffer, and 2 μL of BP recombinase®.
2. Incubate at RT for at least 1 h.
3. Transform the recombination reaction mix in chemically competent bacteria and select transformants on 1 Zeocinsupplemented LB Agar plates at 37 C.
4. Next day, inoculate a few transformants in 3 mL of 1 Zeocinsupplemented liquid LB. Incubate overnight at 37 C and
recover the bacterial cells in 1.5 mL microtubes by a 10 min
centrifugation at 16,000 g.
5. Purify the DNA clones using a DNA miniprep extraction kit
and following instructions provided by the kit manufacturer.
Y2H Screening of PDZ Interactions
9
6. Check for the presence of bait DNA fragments among the
purified recombinant pDONr/pZeo® clones. This can be easily
assessed through a BsrGI restriction analysis as both attB1 and
attB2 sites harbor a BsrGI restriction site. Assuming that the
bait DNA sequence itself contains no BsrGI site, restriction
with this enzyme should release DNA fragments with a size
close to that of the amplified bait products.
7. In addition to the restriction analysis it is strongly recommended to verify the DNA sequence of a few bait-containing
pDONr/pZeo® entry clones by performing a DNA sequencing with M13-For and/or M13-Rev primers.
3.3 Cloning
of the Bait Constructs
in pGBT9-GW
This protocol explains how to transfer bait DNA fragments
between pDONr/pZeo® entry clones and pGBT9-GW, the destination Y2H vector in which the bait genes will be fused downstream of the GAL4-DB domain.
1. Mix 300 ng of the relevant Bait-pDONr/pZeo® entry clone
with 250 ng of pGBT9-GW. Add 2 μL of TE buffer and 2 μL of
the LR recombinase and let the reaction incubate for at least
1 h on the bench.
2. Transform the recombination mix in chemically competent
bacteria and select transformants by plating bacterial cells on
1 ampicillin-supplemented LB-Agar plates for 16 h at 37 C.
3. Inoculate a few colonies in 3 mL of 1 ampicillinsupplemented liquid LB and grow them overnight at 37 C.
4. Proceed with the purification of the plasmid DNA and with the
restriction analysis as described in Subheading 3.3 (steps 5 and
6) to identify bait-containing pGBT9-GW clones.
3.4 Yeast
Transformation
of the Bait Constructs
The procedure to transform pGBT9-GW vectors harboring the
wild-type (WT) or mutant (ΔPBM) bait constructs in the AH109
yeast strain is explained below.
1. Inoculate a colony of AH109 yeast cells in 3 mL of YPD liquid
medium and incubate overnight at 30 C under vigorous
orbital agitation.
2. The next day use the preculture to inoculate 30 mL of YPD
liquid medium at a 1:100 (v/v) dilution.
3. When the culture reaches an OD600 of 0.6 to 0.8, spin down
the cells at 800 g for 8 min, discard the supernatant, and
resuspend the cell pellet in 30 mL of sterile ddH2O by pipetting up and down.
4. Spin down the cells as indicated in step 3.
5. Prepare 50 mL of 1 TE–LiAc solution using the 10 TE
buffer and 10 LiAC stock solutions.
10
Monica Castro-Cruz et al.
6. Resuspend the cells in 15 mL of 1 TE–LiAc.
7. Centrifuge the cell suspension as described in step 3.
8. Resuspend the cell pellet in 1 mL of 1 TE–LiAc by pipetting
up and down.
9. In two sterile 1.5 mL microtubes, distribute in each tube 5 μL
of ssDNA salmon sperm carrier DNA, 100 ng of either the
wild-type or mutant (ΔPBM) bait DNA plasmids, 30 μL of
AH109 cell suspension from step 8 and 100 μL of freshly
prepared PEG–TE–LiAc solution (see Note 1).
10. Homogenize by vortexing thoroughly the cell suspension and
incubate for at least 1 h at 30 C.
11. Add DMSO to a 10% final concentration to each tube (see Note
2) and give a 15 min heat shock to the cells by placing them in a
42 C water bath.
12. Spin down the cells at 800 g for 8 min and discard the
supernatant.
13. Resuspend the cells in 150 μL of sterile ddiH2O.
14. Plate 50 μL of the cell suspension on Agar SC-Trp plates.
15. Incubate the plates for 2–3 days at 30 C until colonies appear.
Plates can be stored at 4 C at this stage.
3.5 Validating
the Bait Constructs
Before screening for potential PDZ partners, we strongly recommend to first check that none of the GAL4BD-bait fusions can
autoactivate the transcription of the GAL1-HIS3 reporter gene by
itself. This can be assessed by either of the two methods outlined
below.
1. A simple and straightforward test consist in streaking
GAL4BD-bait-containing AH109 Trp+ transformants directly
on SC -Trp -His medium and monitoring cell growth after for
2–3 days of incubation at 30 C. Autoactivation of the His3
reporter gene by the GAL4BD-bait fusion will result in good
growth of all of the cells in the streak. In case of ambiguous
growth results, AH109 cells transformed with an empty
pGBT9 vector may prove useful as a convenient negative
control.
2. An alternative and slightly more rigorous method is to assess
the autoactivation capacity of a bait under the same cellular and
growth conditions as those used for the screening of the library,
that is, by mating the Trp+ transformants that contain the bait
to be tested to Y187 cells transformed with the empty pACT2
vector.
3. An easy way to proceed with the mating step is to mix the two
mating partners with sterile tooth-picks in 250 μL of complete
YPD medium in a sterile 1.5 mL microtube.
Y2H Screening of PDZ Interactions
11
4. Incubate the conjugating cells for 5–6 h at 30 C.
5. Spin down the cells by a brief (2–3 min) centrifugation at
medium speed in a microcentrifuge.
6. Wash the cells once with 1 mL of sterile ddH2O before resuspending the cell pellet in 100 μL of sterile ddH2O.
7. Spot 5 and 25 μL of the cell suspension on both SC -Leu -Trp
and SC -Leu -Trp -His solid media.
8. Test for growth after a 2–3 day of incubation at 30 C.
9. Growth on SC -Leu -Trp medium is a readout for the mating
efficiency while, as already discussed in step 1, the ability to
grow on SC -Leu -Trp -His medium directly reflects the bait
tendency to self-activate GAL1-HIS3 transcription.
3.6 Yeast
Two-Hybrid Screening
of PDZome Array
Each clone of the human PDZome library used in this chapter has
been subcloned in pACT2-GW before being transformed in the
Y187 yeast strain and expanded in a three 96-well plate format
(Fig. 2). A frozen stock of this Y2H PDZome resource has been
made, and here we describe the successive steps when implementing a new screen of the resource (see Note 3).
1. Thaw the frozen Y2H PDZome array by placing the three 96w
stock plates on ice for 30 min.
2. Duplicate the Y2H PDZome library with a multichannel
pipette by spotting 5 μL of each clone on SC -Leu agar medium
(see Note 4).
3. Incubate the plates at 30 C until all spots exhibit robust
growth. This step generally takes 2–3 days but may take a few
more days. You may now proceed with the screening as illustrated in Fig. 3 and as detailed below.
4. Day1, distribute 200 μL of SC -Leu medium per well in three
96-deep-well plates and duplicate the Y2H PDZome by inoculating each well with a Y187 PDZ clone from the agar plates
obtained in step 3 (see Note 5).
5. Seal the plates with an adhesive aluminum foil and let the cells
grow for 2 days at 30 C under vigorous agitation.
6. Day2, inoculate 8 mL of SC-Trp liquid medium with baitbearing AH109 strains (WT and PBM mutant) and incubate
overnight at 30 C under agitation.
7. Day3, dispense 150 μL of YPD complete medium per well in
three 96 well plates (see Note 6).
8. Distribute in each well 20 μL of the bait-containing AH109 cell
culture obtained in step 6.
12
Monica Castro-Cruz et al.
Fig. 3 Outline of the yeast two-hybrid protocol. (a) The haploid AH109 (a) and Y187 (α) yeasts containing the
bait and the prey constructs, respectively, are mixed and incubated to allow for the formation of diploid yeasts
expressing now both fusion proteins (a/α). (b) Diploid yeasts containing both prey and bait are selected for
growth on -Leu -Trp SC agar medium. This provides the conditions to verify the mating efficiency and the
formation of diploid yeasts. (c) Simultaneously, the phenotypic test of interactions was performed in selective
culture -Leu -Trp -His SC agar medium. This provides the readout for a possible interaction between the tested
PBM and a given PDZ clone as white dense colonies. Of note, these conditions are valid when studying
conventional, that is, C-terminally located, PDZ-binding motif (PBM), bait constructs corresponding to the wild
type (WT) and truncated (ΔPBM) C-terminal region of a protein of interest are screened in parallel against the
whole PDZome array to verify the requirement of the C-terminal PBM for the observed interaction
Y2H Screening of PDZ Interactions
13
9. Duplicate the library by transferring 30 μL of each of the PDZ
clones from step 5 in a YPD-containing well of the 96w plates
prepared in step 7.
10. Tightly seal the plates with an adhesive aluminum foil and let
the cells mate for at least 5 h at 30 C under gentle (140 rpm)
agitation.
11. Eliminate the medium by spinning down the cells for 10 min at
800 g in a centrifuge equipped with swinging plate carriers.
Using a multichannel pipette, wash the cell pellets once with
200 μL of sterile ddH2O before resuspending them in 20 μL of
sterile ddH2O.
12. For each PDZ clone, spot 5 μL of the mating cell suspension
on SC -Leu, -Trp and 8 μL on SC -Leu, -Trp, -His plates.
13. Incubate all SC Agar plates at 30 C until colony growth can be
assessed on the different media (Fig. 3). Check mating efficiency by monitoring growth on SC -Leu, -Trp medium 2 to
3 days after plating. Growth on SC -Leu, -Trp, -His medium
usually takes 2 to 3 more days and may vary to a large degree
depending on the strength of the Y2H interaction (see Note 7).
14. Scan all the plates and score the positive clones that emerged
on the readout conditions, that is, SC -Leu, -Trp, -His agar
plates, with the WT bait and not with the ΔPBM mutant (see
Notes 8 and 9).
15. Duplicate the screening experiment in the same conditions.
4
Notes
1. Because the PEG–TE–LiAc solution is quite viscous, cutting
the end of a tip with a sterile razor blade greatly helps pipetting
of this solution. Also try to go slowly when giving several
rounds of up and down pipetting to homogenize the cells in
this solution.
2. DMSO is used to enhance DNA entry but can prove quite toxic
to the cells and may trigger cell death, in which case we recommend to avoid including this reagent during the heat shock
procedure.
3. When manipulating the whole Y2H PDZome array, the use of a
multichannel pipette is strongly recommended as it greatly
facilitates handling and helps maintaining the 96-well plate
format of the array. Operating this way will also greatly contribute to reducing mistakes in the identification of positive
clones.
14
Monica Castro-Cruz et al.
4. 150 mm diameter plates should be favored as their large size
makes the 96-well plate format of the PDZome array easy to
reproduce on solid agar medium.
5. If thawing of the Y2H PDZome array on SC -Leu agar plates
has been performed with a multichannel pipette the use of the
same pipet loaded with sterile tips to scratch the clone surface
and to inoculate the 200 μL of SC -Leu liquid medium will
greatly facilitate this step.
6. Performing the mating step in 96-well plates greatly facilitates
the handling by enabling the use of multichannel pipettes.
7. Depending on the bait used for the screening, it may be useful
to optimize the temperature for both the mating and plates
growth. The optimal temperature may vary between 25 C and
30 C.
8. When using baits of several tens or hundreds amino acid long,
the occurrence of positive PDZ signals with both the WT and
truncated bait constructs may indicate the presence of an internal PDZ-binding motif within the bait.
9. 3-Amino-1,2,4-triazole (3-AT), a potent histidine analog, may
be added to the SC -Leu -Trp -His selective medium whenever
more stringent screening conditions are required in order to
reduce potential false positives. The 3-AT optimal concentration can vary substantially between yeast strains and needs to be
worked out but generally falls within the 1-25 mM range.
Acknowledgments
Through the European PDZnet consortium, the JPB and PZ
laboratories have received funding from the EU Horizon 2020
RIA under the Marie Skłodowska-Curie grant agreement
No. 675341. Work in the laboratory of PZ is currently supported
by the Fund for Scientific Research–Flanders (FWO Grants
G.0846.15 and G0C5718N), the Agence Nationale de la
Recherche (ANR-18-CE13-0017, Project SynTEV) and receives
funding from the European Union’s Horizon 2020 research and
innovation program under the Marie Sklodowska-Curie grant
agreement No 747025. JPB is a scholar of Institut Universitaire
de France.
Y2H Screening of PDZ Interactions
15
References
1. Fields S, Song O (1989) A novel genetic system
to detect protein–protein interactions. Nature
340:245–246
2. Brückner A, Polge C, Lentze N, Auerbach D,
Schlattner U (2009) Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci
10:2763–2788
3. Zhu ZX, Yu ZM, Taylor JL, Wu YH, Ni J
(2016) The application of yeast hybrid systems
in protein interaction analysis. Mol Biol
50:663–670
4. Rodrı́guez-Negrete E, Bejarano ER, Castillo
AG (2014) Using the yeast two-hybrid system
to identify protein–protein interactions. In:
Jorrin-Novo JV, Komatsu S, Weckwerth W
et al (eds) Plant proteomics: methods and protocols. Humana Press, Totowa, NJ, pp
241–258
5. Fields S (2005) High-throughput two-hybrid
analysis. The promise and the peril. FEBS J
272:5391–5399
6. H€auser R, Stellberger T, Rajagopala SV, Uetz P
(2012) Matrix-based yeast two-hybrid screen
strategies and comparison of systems. Methods
Mol Biol 812:1–20
7. Galletta BJ, Rusan NM (2015) A yeast
two-hybrid
approach
for
probing
protein–protein interactions at the centrosome. Methods Cell Biol 129:251–277
8. Lenfant N, Polanowska J, Bamps S, Omi S,
Borg JP, Reboul J (2010) A genome-wide
study of PDZ-domain interactions in
C. elegans reveals a high frequency of
non-canonical binding. BMC Genomics
11:671
9. Rajagopala SV, Sikorski P, Caufield JH,
Tovchigrechko A, Uetz P (2012) Studying
protein complexes by the yeast two-hybrid system. Methods 58:392–399
10. Luck K, Charbonnier S, Travé G (2012) The
emerging contribution of sequence context to
the specificity of protein interactions mediated
by PDZ domains. FEBS Lett 586:2648–2661
11. Songyang Z (1997) Recognition of unique
carboxyl-terminal motifs by distinct PDZ
domains. Science 275:73–77
12. Belotti E, Polanowska J, Daulat A, Audebert S,
Thomé V, Lissitzky J-C, Lembo F, Blibek K,
Omi S, Lenfant N, Gangar A, Montcouquiol
M, Santoni M-J, Sebbagh M, Aurrand-Lions
M, Angers S, Kodjabachian L, Reboul J, Borg
J-P (2013) The Human PDZome: A Gateway
to PSD95-Disc Large-Zonula Occludens
(PDZ)-mediated Functions. Mol Cell Proteomics 12:2587–2603
Chapter 2
Identification of PDZ Interactions by Affinity Purification
and Mass Spectrometry Analysis
Avais M. Daulat, Stéphane Audebert, Mônica Wagner, Luc Camoin,
and Jean-Paul Borg
Abstract
Identification of protein networks becomes indispensable for determining the function of a given protein of
interest. Some proteins harbor a PDZ binding motif (PDZBM) located at the carboxy-terminus end. This
motif is necessary to recruit PDZ domain proteins which are involved in signaling, trafficking, and
maintenance of cell architecture. In the present chapter, we present two complementary approaches
(immunopurification and peptide-based purification procedures) followed by mass spectrometry analysis
to identify PDZ domain proteins associated to a given protein of interest. As proof of example, we focus our
attention on TANC1 which is a scaffold protein harboring a PDZBM at its carboxy-terminus. Using these
two approaches, we identified several PDZ domain containing proteins. Some of them were found with
both approaches, and some were specifically identified using peptide-based purification procedure. This
exemplifies advantages and differences of both strategies to identify PDZ interactions.
Key words PDZ, Purification, Protein complexes, Proteomics
1
Introduction
Over the last 20 years and following the seminal work of Bertrand
Séraphin and colleagues, proteomic methods deciphering the composition of protein complexes associated to proteins of interest have
become indispensable for determining their functions [1]. They
have benefited from the tremendous efforts made to improve the
sensitivity of mass spectrometry (MS) equipment and from the
development of versatile purification procedures such as proteinor peptide-based purification protocols [2]. These strategies of
purification are fast (less than 24 h) and allow the copurification
of low affinity associated proteins such as those mediated by PDZ
domains (Fig. 1). It is indeed well known that Postsynaptic density
Avais M. Daulat and Stéphane Audebert contributed equally to this work.
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021
17
18
Avais M. Daulat et al.
Immunopurification:
Peptide-based purification:
Protein:
PDZ binding
motif
FLAG tag
Peptide:
Quality control 10%
90%
Gel stack
Associated
proteins
+
PDZ binding
motif
Protein lysate
Immobilization
Cross linking
Beads
Non specific
proteins
Beads
ANTI-FLAG® M2
Affinity Gel
Beads
Silver staining
Binding
Mass spectrometry
analysis
Relatiive
abundance
Beads
432.2
Beads
810.7
200
Washing
456.3
286.8
400
m/z
600
800
Washing
-Log10 (p-value)
Beads
Quantification
Beads
log2 (Bait vs Ctl)
Protein network
Elution
Elution
Fig. 1 Affinity purification procedure. (Left) A protein of interest is expressed as a fusion protein with a FLAG
sequence at its N-terminus. Stable cell lines expressing the bait are lysed and proteins are purified with antiFLAG antibodies coated on beads using an one step purification procedure. (Right) Peptide are bound to beads
and used as preys to purify interacting proteins from cell lysates. (Center) purified proteins are processed to be
analyzed by MS and quantified to build a protein network
protein-95/Disks large/Zonula occludens-1 (PDZ) domain proteins bind to their interactors with a low affinity ranging from 1 to
100 μM [3]. In the human proteome, 152 proteins harbor one or
more PDZ domains which bind to a hydrophobic motif located at
the C-terminus of their cognate interactors [4]. Mutation or deletion of the last three amino acids of this motif leads generally to
complete loss of interaction. The role of PDZ domain proteins are
multiple, from the scaffolding of receptors and signaling proteins to
promote efficient signal transduction to the establishment and
maintenance of cellular architecture required for epithelial and
neuronal tissue functions.
In the present chapter, we present a general procedure to
identify PDZ domain proteins associated to TANC1, a protein of
interest bearing a carboxy-terminal PDZ binding motif (PDZBM).
TANC1 is a postsynaptic scaffold protein [5] which regulates spatial
memory and embryonic development [6]. It contains TPR and
ankyrin repeat domains and a PDZBM (SNV) located at its
C-terminus (Fig. 2a). Our approach is based on the comparative
purification of protein complexes associated to wild type TANC1 or
Identification of Associated PDZ Proteins
A)
896
19
1272 1289 1403
1
1861 a.a.
SNV
Ankyrin repeat
region
TPR repeat
region
PDZ binding motif
B)
FLAG-TANC1
DAPI
DAPI
Fig. 2 Description and expression of TANC1, a PDZBM containing protein. (a) TANC1 protein is composed by
1601 amino acids with eleven ankyrin repeats, three TPR repeats and a PDZ binding motif located at its
C-terminus. (b) Subcellular localization of FLAG-TANC1. HEK293T expressing FLAG-TANC1 (left) or a carrier
plasmid (right) are grown on coverslips pretreated with poly-L-lysine to let the cells to attach. After 48 h, cells
are fixed with paraformaldehyde 4%, permeabilized using Triton X-100 at 0.4% and labeled with anti-FLAG
antibody and DAPI
to a mutant form lacking the PDZBM in order to identify PDZ and
non PDZ-mediated interactions. In our lab, we systematically combine side by side immunopurification and peptide-based purification procedures to identify PDZ proteins interacting with a
PDZBM containing protein, here TANC1.
2
Materials
2.1 Common
Reagents and Buffers
1. HEK293T cells from ATCC® Number: CRL-3216™.
2. Dulbecco’s modified Eagle’s medium (DMEM), 4.5 g/L glucose, 100 U/mL penicillin, 0.1 mg/mL streptomycin, and
1 mM glutamine) (Thermo Fisher Scientific).
3. PBS.
20
Avais M. Daulat et al.
4. NuPAGE® Pre-Cast Gel System, 4–12% Bis/Tris Gel.
5. NuPAGE® LDS Sample Buffer (Thermo Fisher Scientific).
6. Refrigerated tabletop centrifuge.
7. Shaker/roller.
8. Speed vacuum concentrator.
9. Protein quantification kit.
10. Imperial protein stain (Thermo Fisher Scientific).
11. Silver stain kit (Thermo Fisher Scientific).
12. Spectrophotometer.
13. Mass spectrometer: Q Exactive Plus Hybrid QuadrupoleOrbitrap online with a nanoLC Ultimate 3000 chromatography system (Thermo Fisher Scientific™, San Jose, CA).
2.2 Basic Reagents
for Immunopurification
1. Puromycin dihydrochloride antibiotic.
2. Poly(ethyleneimine) solution, Transfection Reagent.
3. pIRES puro 3 vector (Clontech).
4. ANTI-FLAG® M2 Affinity Gel (Sigma-Aldrich).
5. Lysis buffer: 10% (v/v) glycerol, 50mM HEPES–NaOH pH
8.0, 150mM NaCl, 2mM EDTA, 0.1% (v/v) IGEPAL
CA-630, 2mM DTT, protease inhibitor cocktail (SigmaAldrich), and phosphatase inhibitor cocktail (Sigma-Aldrich).
6. Specific antibodies against the protein of interest and known
interacting proteins.
7. Monoclonal ANTI-FLAG® M2 antibody produced in mouse
(Sigma-Aldrich).
8. NuPAGETM LDS 2X Sample Buffer (Thermo Fisher
Scientific).
9. 3x FLAG® peptide (Sigma-Aldrich).
2.3 Basic Reagents
for Peptide-Based
Purification
1. NHS (N-hydroxysuccinimide) activated Sepharose 4 Fast Flow
(GE Healthcare Life Sciences).
2. Isopropanol.
3. 100 mM MES (2-(N-morpholino)ethanesulfonic acid sodium
salt), pH 6.8.
4. 1 mM HCl.
5. 200 mM ethanolamine, pH 8.
6. 100 mM Tris–HCl pH 8.
7. 100 mM Sodium acetate pH 4.
8. Preservative solution: PBS with 0.2% azide.
9. Synthetic peptides.
Identification of Associated PDZ Proteins
21
10. Streptavidin Sepharose High Performance affinity resin
(GE Healthcare Life Sciences).
11. 20 mM biotin.
2.4
Sample Digestion
1. Water 18 ohm grade.
2. Formic acid.
3. CH3CN, LCMS grade.
4. Hydration buffer: 100 mM of ammonium bicarbonate (AmBi)
5. Washing buffer 1: 100 mM AmBi–CH3CN (50:50) (v/v).
6. Washing buffer 2: 25 mM AmBi–CH3CN (50:50) (v/v).
7. Reduction buffer: 10 mM DTT in 0.1 M Ambi.
8. Alkylation buffer: 55 mM iodoacetamide in 0.1 M Ambi.
9. Trypsin sequencing grade.
10. 96-well polypropylene plate with conical bottom.
11. Peptide extraction buffer: CH3CN–formic acid 5% (60:40)
(v/v).
3
Methods
3.1 Immunopurification
3.1.1 Mammalian
Expression Vectors
3.1.2 Verification
of Protein Expression
by Western Blot
For the identification of TANC1 protein complexes, the TANC1
cDNA was cloned downstream of an N-terminal FLAG tag within
pIRES-puro3 plasmid (see Note 1) using polymerase chain reaction
amplification and standard molecular biology procedures. To identify protein complexes specifically associated with the PDZBM of
TANC1, we mutated the cDNA of TANC1 in order to code for the
AAA sequence instead of the SNV sequence at the carboxyterminus of TANC1.
Once the sequence integrity is validated, we generally test for
protein expression by western blot analysis following transient
transfections of mammalian cells. Although any mammalian cells
can be used, we routinely use HEK293T cells for this purpose since
they can be efficiently transfected (see Note 2).
1. Grow HEK293T cells in two 10-cm plates up to 40–50%
confluency.
2. For the transfection, mix 5 μg of empty or pIRES-puro vector
expressing the protein of interest with 15 μL of polyethyleneimine (PEI) solution, that is, in a ratio of 1 μg of DNA for 3 μL
of PEI (see Note 3).
3. Probe cell extracts by western blotting for the expression of the
fusion protein using anti-FLAG antibodies.
22
Avais M. Daulat et al.
3.1.3 Mammalian Stable
Cell Lines
In order to generate a stable cell line expressing the desired FLAGtagged protein as in the case of TANC1:
1. Transfect HEK293T cells as described above (see Note 4).
2. Forty-eight hours after transfection, rinse the cells once with
PBS and dissociate with 1mL of trypsin–EDTA.
3. Resuspend trypsinized cells in 10 mL of DMEM cell growth
media.
4. Half of the resuspended cells are transferred in a 15-cm petri
dish containing 20 mL of DMEM–FBS medium supplemented
with 2 μg/mL of puromycin (see Note 5).
5. Stable cell line is then isolated by replacing the selective media
every 2–3 days or more often depending on the rate of cell
death observed.
3.1.4 Characterization
of Stable Cell Lines
Polyclonal stable cell lines can be obtained within 2 weeks of
selection. Clonal selection can take up to 4 weeks due to the
isolation of unique cells in 96-well plate and subsequent clonal
amplification. Immediately after obtaining the stable cell lines,
carry out the following steps:
1. Prepare frozen vials and store the cells adequately.
2. Regularly reassess the level of expression of the fusion proteins,
particularly before large-scale purification.
3. Subcellular localization of the constructs can also be checked
by immunostaining and confocal imaging to confirm the correct localization of the given FLAG-tagged protein of interest.
For example, TANC1 has a cytosolic localization when
expressed in HEK293T cells (Fig. 2b).
3.1.5 Amplification
of Expressing Cells
for Large-Scale Purification
To obtain the adequate quantity of protein complexes for successful
detection by MS, it is recommended to have relatively large amount
of starting material.
1. Start the expansion of stably transfected cells by transferring
one confluent 10-cm cell culture Petri-dish into two 15-cm
plates.
2. Then expand to ten 15-cm plates. More plates may be used if
necessary (see Note 6).
3. In parallel, prepare the same quantity of HEK293T cells stably
transfected with an empty pIRES-puro vector and process
them in a similar way throughout the purification procedure.
3.1.6 Preparation of Cell
Extracts
and Immunopurification
Although different procedures can be applied to harvest the cells,
for HEK293T cells, we use the following procedure. All procedures
are performed at 4 C and all buffers are prechilled on ice (see Notes
7 and 8).
Identification of Associated PDZ Proteins
23
1. Wash the cells carefully in the dish using ice-cold PBS. Carefully
remove the media and wash the cells with 10 mL of PBS.
Repeat this step three times.
2. Add 2 mL of ice-cold lysis buffer per plate. Scrape the cells
using cell scrapers to favor membrane crushing and carefully
collect in 15 mL conical tubes. Tubes are then placed on a
rotator for 30 min to 1 h (see Note 9).
3. To guarantee complete lysis of the cells, proceed to one freeze–
thaw cycle using liquid nitrogen (see Note 10).
4. Thaw the lysates and aliquot them into ten 1.5 mL microfuge
tubes. Spin at 15,000 g for 15 min at 4 C. Collect the
supernatant which consist of the soluble fraction into a
15 mL conical tubes (see Note 11).
5. Equilibrate 20 μL of packed ANTI-FLAG® M2 Affinity agarose
beads by three 1 mL washes with lysis buffer (protein and
phosphatase inhibitors are not required).
6. Spin down beads at 800 g for 1 min. To carefully remove the
supernatant and not the beads, we use a clean 27-gauge needle
attached to the vacuum system.
7. Transfer the beads to the 15 mL conical tube containing the
soluble protein fraction and incubate on a rotator overnight at
4 C. We generally prepare the lysates in the afternoon and
incubate them on beads overnight.
8. Spin down at 800 g the beads and discard the supernatant.
9. Wash the beads once with 10 mL of lysis buffer and transfer
them into a 1.5 mL microfuge tube to perform 3 additional
washes using 800 μL of lysis buffer (see Note 12).
3.1.7 Elution
Since the presence of detergents is not compatible with MS, it is
important to eliminate detergents. For this purpose either of the
three options listed below can be used.
1. Option A: Elute proteins bound to ANTI-FLAG® M2 Affinity
agarose beads by using 20 μL of NuPAGETM LDS 2 sample
Buffer
2. Heat the beads at 70 C for 10 min.
3. Load 10% of the eluted fraction on an electrophoresis
NuPAGE® pre-cast gel system (4–12%).
4. After migration, gels are silver stained using protocols compatible with mass spectrometer [7] or using commercially available
kit such as Pierce™ Silver Stain Kit (Fig. 3a).
5. Option B: Elute proteins using 3xFLAG® peptide using five
column-volumes (~100 μL) solution at 100 μg/mL in 50 mM
ammonium bicarbonate, pH8.0.
A)
Avais M. Daulat et al.
C
-T
AN
AG
FL
FL
AG
-T
AN
C
1
1
(-A
A
A)
Co
nt
ro
l
24
B)
TANC1
TANC1-AAA
TANC1
AGAP3
C10
SET
TRIM41
AGAP1
ANP32A
NFATC3
SCRIB
DLG1
250 kDa
MPP7
CASK
PTPN13
130 kDa
SNX27
DDB1
MAP1B
100 kDa
HADHA
APC
VPRBP
70 kDa
USP9X
CTNNB1
TNIK
APPBP2
SPECC1L
55 kDa
HADHB
PPP6R3
CTNNA1
PJA2
KCTD5
COPA
PHGDH
35 kDa
MINK1
AKAP11
SDCCAG3
HTATSF1
CPVL
FAM190B
PRKACA
RASAL2
PPP1R12A
SNX9
USP7
SNX33
POLR2B
ANP32B
GTF2I
NACA
SDC2
PPP2R2A
HIST1H2BC
AMOT
FKBP3
CTNND1
MSH6
Fig. 3 Identification of FLAG-TANC1 partners by immunoprecipitation. (a)
SDS-PAGE separation of protein complexes associated to FLAG-TANC1 purified
by immunoprecipitation. Purified protein complexes associated to wild type
FLAG-TANC1 (left lane) or its mutant form (right lane) were separated on
4–12% NuPAGE® acrylamide gel and silver stained. (b) List of proteins associated to TANC1 through its PDZ binding motif. PDZ domain proteins are in red
Identification of Associated PDZ Proteins
25
6. Option C: Purified proteins can also be recovered by trypsin
digestion directly on the ANTI-FLAG® M2 Affinity agarose
beads.
7. Wash the beads three times with 200μL of 50 mM ammonium
bicarbonate, pH8.0
8. Resuspend the beads in 40 μL of 50 mM ammonium bicarbonate before trypsin digestion.
Of note, 10% of the eluted material will be kept for quality
control purposes (gel separation and silver staining). The remaining
90% will be used for MS analysis (Fig. 1).
3.2 Peptide-Based
Purification
Peptide pulldown consists of linking a synthetic peptide to a matrix
which consists of agarose beads in general. As a PDZBM usually
corresponds to the carboxy-terminus end of the peptide mimicking
the carboxyl end of protein, it is crucial to let this side free and then
use the best strategy to cross link the beads via the N-terminus end
of the peptide. For this purpose, we recommend to use NHS
activated beads to covalently couple peptides via their primary
amino terminal group. This chemical reaction is easy to perform
and chemically stable (see Note 13).
If the synthesized peptide is composed of an internal lysine
(s) within the sequence or near the PDZBM, NHS coupling may
occur on its lateral chain and may impair the PDZBM–PDZ interaction. In this particular case, we recommend to use N-terminal
biotinylated peptides which can be coupled to streptavidin beads
(see Note 14).
3.2.1 Design of Peptide
Containing a PDZBM
1. Use a 15-mer peptide corresponding to the 15 last amino acids
of the protein which encompasses the PDZBM (see Note 15).
2. As a control, use a 12-mer peptide with the same sequence but
lacking the last tree amino acids.
3. The amino terminus should be free or biotinylated in case of
using NHS activated beads or streptavidin beads, respectively.
4. The carboxy-terminal side should be a carboxyl group
(-COOH).
5. Purity of the peptide should be above 95%.
6. Minimal quantity of peptide should be above 4mg.
3.2.2 Coupling
the Peptide to NHS Beads
All solutions should be cooled at 4 C. This protocol is adapted to
prepare 0.5 mL of peptides coupled to beads (theoretical concentration: 2 μmole peptides/mL of packed beads). A typical peptide
pulldown experiment should include at least three conditions
(Fig. 4a), namely, beads with wild-type PDZBM peptide, beads
with a mutant PDZBM peptide, and beads with no peptide.
Avais M. Daulat et al.
ΔP
D
Z
BM
26
C
C
ot
ot
Bi
Bi
-T
AN
-T
AN
s
ad
Be
1
2
3
tl
C
Mr (kDa)
1
1
A)
200
116
97
66
55
36
31
21
B)
MAGI3
MPP2
DTNA
INADL
-Log10 (p-value)
TTC8
MPDZ
CPVL GORASP2 SNX27
MPP7
UTRN
MPP5
LNX2
SNTB1
LIN7A
SNTA1
RAPGEF6
ERBB2IP
DLG3
SEC23A
HSPA1B
SYNJ2BP
UBR3 CCDC47PTPN13
HSPA8
PPP1CA
NUP35
DMD
PPP1CC
CTNNA
CASK
SCRIB
DLG1
MPP6
SNTB2
MAGI1
DNTB
LIN7C
TAX1BP3
log2 (TANC1 vs TANC1ΔPDZ)
Fig. 4 Identification of TANC1 PDZBM partners by peptide pulldown. (a) SDS-PAGE separation of protein
complexes associated to TANC1 peptides. Purified proteins obtained from pulldown with control beads (left),
beads coupled to TANC1 peptide (middle) or to TANC1 lacking the last three amino-acids (right) were
Identification of Associated PDZ Proteins
27
1. Lyophilized peptides are solubilized at 10 mM according to the
manufacturer instructions and physicochemical properties of
the peptide sequences. Ideally, only water should be used, but
addition of 10 mM NaOH or HCl can help in solubilization of
peptides with acidic or basic properties, respectively. Use
DMSO in case of peptides with hydrophobic sequences. Solutions that contained primary amines are prohibited as primary
amines will interfere with cross-link reactions. Keep at 4 C
until use and then aliquot and store at - 80 C.
2. Using a 1ml tip cut at its extremity transfer the equivalent of
0.5ml of packed NHS activated Sepharose 4 Fast Flow beads in
15 mL-conical tubes containing 2–3 mL of cold isopropanol.
3. Centrifuge at 800 g for 5 min, 4 C, check the volume
corresponding to sedimented beads and adjust by adding
beads if needed. When 0.5 mL of packed beads is reached,
store the microfuge tube at 4 C until use.
4. Mix 100 μL of solubilized peptides with 900 μL MES buffer, in
a 1.5-mL microfuge tube and keep at 4 C.
5. Repeat this operation for all peptides that should be crosslinked.
6. Do an extra condition without peptide corresponding to control beads alone.
7. Keep microfuge tubes containing diluted peptides and control
at 4 C.
8. The next steps should be done extremely rapidly and at 4 C as
NHS beads will be active as soon as it will be in aqueous
solutions.
9. For all tubes, remove the isopropanol supernatant above the
beads.
10. Add 10 mL of cold 1 mM HCl.
11. Mix rapidly but gently without vortexing to resuspend the
beads.
12. Centrifuge at 800 g for 5 min, 4 C.
13. Remove the supernatant.
14. Transfer the peptide or control solutions made in steps 4–6 to
the beads prepared in step 2.
15. Mix rapidly.
16. Let on wheels overnight (see Note 16).
ä
Fig. 4 (continued) separated on 4–12% NuPAGE® acrylamide gel and silver stained. (b) Proteins associated to
TANC1 peptides and identified by MS. Volcano plots showing differential Log2 (peptide intensity) levels (x axis)
and –Log(p-value) (y axis) for TANC1 versus TANC1ΔPDZBM
28
Avais M. Daulat et al.
17. Add 3 mL of ethanolamine to quench the reaction and keep on
wheels for 3 h.
18. Centrifuge at 800 g for 5 min, remove the supernatant, add
10 mL of Tris, and mix gently.
19. Centrifuge at 800 g, for 5 min, remove the supernatant, add
10 mL of Acetate, mix gently.
20. Repeat the last two steps twice.
21. Centrifuge at 800 g for 5 min, remove the supernatant, add
10 mL of PBS, and mix gently.
22. Repeat the last operation twice.
23. Centrifuge, remove the supernatant, and add to 0.5 mL dried
beads 4.5 mL of preservative solution for short-term storage
(see Note 17). Beads are ready to be used for peptide pulldown.
24. Just before pulldown, add 200 μL of the bead suspension,
corresponding to 20 μL of dried beads, to a clean microfuge
tube and centrifuge gently.
25. Equilibrate the beads once with 1 mL of lysis buffer used to
treat the cells or tissues.
26. Remove supernatants until 3–5 mm above the sedimented
beads to avoid drying.
3.2.3 Coupling
Biotinylated Peptides
with Streptavidin Beads
1. Streptavidin beads are always washed the day before the experiment with PBS to remove ethanol from the commercial stock.
2. Twenty microliters of dried beads will be used for each experimental point. Determine the volume you will need and add a
calculated extra volume to be sure to keep enough beads after
extensive washes.
3. To a 15-mL-conical tube, add 3–5 mL PBS.
4. Transfer the volume of streptavidin beads needed to the experiments to the 15 mL-conical tube and centrifuge at 800 g for
5 min at 4 C.
5. Discard the supernatant, check the bead volume, and adjust if
needed.
6. After the last centrifugation, add adequate volume of PBS to
obtain a 10% beads suspension (v/v).
7. Prepare adequate number of microfuge tubes (one per condition plus one extra microfuge tube corresponding to the control without peptide).
8. Add 500 μL PBS in a 1.5 mL microfuge tube and add 200 μL
of 10% beads suspension (20 μL packed beads) (v/v), centrifuge at 800 g for 5 min, and confirm the volume of packed
beads. Beads are now ready to use.
Identification of Associated PDZ Proteins
29
9. Add for each condition 10 μL of 10 mM biotinylated peptides
or 10 μL of 10 mM biotin (control without peptide).
10. Incubate for 1 h on rotating wheels at 4 C.
11. Wash the beads twice with 1 mL PBS to remove unbound
peptides.
12. Discard the supernatant. Leave 3–5 mm of PBS above the
packed beads to avoid beads drying. Beads are now ready for
the peptide pulldown.
13. Add 20% ethanol (v/v). Peptide-coupled beads prepared with
this protocol can be stored for at least 1 year, meaning that a
large stock (1–2 mL Sepharose beads) can be prepared for
further use. In contrast, the half-life of streptavidin beads is
shorter. We thus recommend preparing extemporaneously
fresh streptavidin beads coupled with peptides.
3.2.4 Peptide Pulldown
1. Preparation of the lysates (see Notes 2 and 18).
2. Lysates should be used at 1–5 mg/mL of soluble proteins (see
Note 19).
3. Lysates should be precleared with control beads such as NHS
beads saturated with ethanolamine (as described in Subheading
3.2.2, step 6) or streptavidin beads (see Note 20), a ratio of
200 μL packed beads for 10 mL lysate is appropriate but can be
adjusted depending on the protein concentration of the lysates.
Preclearing is performed at 4 C on wheels for 45 min.
4. Centrifuge the lysates at 3000 g for 5 min to sediment the
beads.
5. Transfer gently the supernatants without any beads in another
tube compatible with a 16,000 g centrifugation.
6. Centrifuge the lysates at 16,000 g for 1 min to be sure to
eliminate extra beads or protein aggregates (see Note 21).
7. Transfer the supernatants to a new tube: the lysates are ready.
8. Transfer 1 mL of the lysates (1–5 mg/mL) in 1.5-mL-microfuge tubes containing beads bearing peptides or control beads.
9. Incubate for 2 h on wheels, at 4 C.
10. Wash the beads 3 to 5 times with 1 mL lysis buffer.
11. Add 20 μL 2 LDS NuPAGE buffer and heat at 70 C for
10 min.
12. Centrifuge at 2000 g for 5 min.
13. Migrate 10% (2 or 3 μL) of the bead eluate on a NuPAGE®
precast gel (4–12%) and perform a silver staining for quality
control of the experiment (Fig. 4a).
14. Store the remaining 90% at 20 C for the MS analysis.
30
Avais M. Daulat et al.
3.3 Trypsin Digestion
of Protein Complexes
and Preparation
for Mass Spectrometry
Analysis
This method is an universal method of sample preparation and
digestion that works with all samples eluted in LDS NuPAGE
buffer and has the main advantage to remove all small putative
contaminants as detergents, small peptides, and salts that could
interfere with protease activities or MS analysis. The methods consist of:
3.3.1 Eluted Sample
Preparation
1. Load 90% of the remaining sample on a 4–12% Bis/Tris gel
2. Stop the electrophoresis migration as soon as the sample has
completely entered the top of the gel (80 V, 5 min).
3. Stain the acrylamide gel overnight with the Imperial protein
stain, according to the manufacturer instructions.
4. The next day, wash the gel extensively with water until a clean
stacking band appears.
5. Stacking bands highlighted with trans illuminator are cut from
gel using a clean scalpel and transferred onto a 96-well polypropylene plate with conical bottoms and stored drained until
reduction–alkylation–digestion steps (see Note 22).
3.3.2 Trypsin Digestion
Once cut, the sample is directly processed for trypsin digestion.
1. In each well, wash the gel pieces with 100 μL of washing buffer
1, incubate for 5 min at room temperature and aspirate the
extra liquid. Repeat once.
2. Add 100 μL of CH3CN, the gel will compact and will become
white, then remove the liquid and let the gel dry for 15 min at
room temperature.
3. Add 50 μL of rehydration buffer and let the gel swell for 5 min.
4. Add 50 μL of CH3CN, wait 15 min and remove the liquid and
let the gel dry for 15 min (see Note 23).
5. The sample is reduced with the addition of 100 μL of reduction
buffer and heated to 56 C for 45 min. Cover the plate with an
adhesive film to avoid contaminations and liquid evaporation.
6. Remove the liquid and replace rapidly with fresh 100 μL of
alkylation buffer and incubate in the dark for 30 min at room
temperature.
7. Liquid is removed from the well, and gel is washed twice with
washing buffer 2. Then the liquid is removed and the gel is dry
off during 15 min at room temperature.
8. The sample is then digested by adding 50 μL of sequence-grade
trypsin prepared at 12.5 ng/μL in digestion buffer.
9. Incubate for 15 min at 4 C
10. Add 50 μL of digestion buffer to fully rehydrate gel and
incubate overnight at 37 C.
11. The day after, add 75 μL of 5% formic acid.
Identification of Associated PDZ Proteins
31
12. Place gently the plate in a sonicator bath and make sure that the
bottom of the 96-well plate is in contact with water, sonicate
for 10 min
13. Carefully, transfer the liquid containing the extracted peptide
to a clean 0.5-mL microfuge tube
14. Add in each well 100 μL of extraction buffer.
15. Sonicate for 10 min and then add the liquid to the previous
tube to pool the extracted peptides. Repeat once.
16. Dry the extracted peptide using a speed vacuum concentrator.
3.3.3 Mass Spectrometry
Analysis
Mass spectrometry analysis is carried out by LC-MSMS using a Q
Exactive Plus Hybrid Quadrupole-Orbitrap online with a nanoLC
Ultimate 3000 chromatography system (Thermo Fisher Scientific™, San Jose, CA) (see Note 24).
1. For each biological sample, inject in triplicate 5 μL
corresponding to 25 % of digested sample on the system.
2. After pre-concentration and washing of the sample on a
Acclaim PepMap 100 column (C18, 2 cm 100 μm i.d. 100
Å pore size, 5 μm particle size), peptides are separated on a LC
EASY-Spray column (C18, 50 cm 75 μm i.d., 100 Å, 2 μm,
100 Å particle size) at a flow rate of 300 nL/min with a
two-step linear gradient (2–22% acetonitrile–H2O; 0.1% formic acid for 100 min and 22–32% acetonitrile–H2O; 0.1%
formic acid for 20 min).
3. For peptides ionization in the EASYSpray source, set spray
voltage at 1.9 kV and the capillary temperature at 250 C. All
samples are measured in a data-dependent acquisition mode.
4. Each run is preceded by a blank MS run in order to monitor
system background.
5. The peptide masses are measured in a survey full scan (scan
range 375–1500 m/z, with 70 K FWHM resolution at m/
z¼400, target AGC value of 3 106 and maximum injection
time of 100 ms).
6. Following the high-resolution full scan in the Orbitrap, the
10 most intense data-dependent precursor ions are successively
fragmented in HCD cell and measured in Orbitrap (normalized collision energy of 25%, activation time of 10 ms, target
AGC value of 1 103, intensity threshold 1 104 maximum
injection time 100 ms, isolation window 2 m/z, 17.5 K FWHM
resolution, scan range 200–2000 m/z).
7. Dynamic exclusion is implemented with a repeat count of 1 and
exclusion duration of 20 s.
32
Avais M. Daulat et al.
3.4 Mass
Spectrometry–Based
Quantification
The bioinformatics analysis can be done according to the following
references using the free software’s MaxQuant and Perseus
described below [8–11]. Alternatively, other software programs
can be used as for example the free academic software Proline
[12] (http://www.profiproteomics.fr/proline/) or a commercial
one, Progenesis QI for proteomics (Nonlinear dynamics a Waters
company). Here, a summary of the bioinformatics analysis of raw
files from MS is given. Only changes from the default parameters
are mentioned (see Note 25).
3.4.1 Protein
Identification
and Quantification Using
MaxQuant
1. Download MaxQuant from https://www.maxquant.org/
website.
2. Install the software on a compatible computer (see Note 26).
3. Load all LC-MS/MS files using “Raw data/Load” (see Note
27).
4. Fulfil your experimental setup in the “Experiment” column.
Select LC-MS/MS file and then click “raw data/Set experiment” and name the line using an unique name for each
technical replicate and biological replicate (e.g., Control1,
Control2, Control3, etc. and Pulldown1, Pulldown2, Pulldown3, etc.).
5. Under “Group-specific parameters/Label-free quantification”
select LFQ for label-free quantification (see Note 28) and set
the “LFQ min. ratio count” to 1 (see Note 29).
6. Download a protein database corresponding to your studied
species from the Uniprot website (https://www.uniprot.org/)
(see Note 30).
7. Load the fasta file in the MaxQuant software using “Global
parameters/Sequences” and do not forget to enter the organism ID using the “Taxonomy ID” button (e.g., 9606 for
Human) and set the parse rule using “Identifier rule” (e.g.,
For Uniprot database use the Uniprot identifier).
8. Under the “Group-specific parameters/Identification” set on
the Match between run option (see Note 31).
9. Under the “Group-specific parameters/Label-free quantification” set on the iBAQ calculation (see Note 32).
10. Under the “Group-specific parameters/Folder locations” set
on the location of the temporary folder and the Combined
folder locations (see Note 33).
11. Set the number of processors according to your computer at
the bottom left of the MaxQuant window. Press “Start” to run
the program and save the parameters files (mqpar.xml) using
the menu “File/Save parameters” in the combined folder (see
Note 34).
Identification of Associated PDZ Proteins
3.4.2 Statistical Analysis
Using Perseus
33
1. Download Perseus from https://www.maxquant.org/per
seus/ website.
2. Load proteinGroups.txt file into Perseus using the generic
upload button (Green arrow at the top left of the Perseus
window) (see Note 35).
3. Select LFQ intensities as expression data by transferring them
in the main box and iBAQ intensities by transferring them in
the numerical box for and press “load” (see Note 36).
4. The matrix 1 open containing all the data necessary for the
statistical analysis. First filter the dataset using “Filter rows/
Filter based on categorical column” and remove rows positive
to “identified by site,” “reverse,” and “potential contaminant”
(see Note 37).
5. Transform intensities in log2 values using “Basic/Transform.”
6. Annotate the data set according to the conditions groups using
“Annot. Rows/Categorical annotation rows”, for example two
groups Control and Pulldown (see Note 38).
7. Filter the dataset to eliminate proteins quantified in only few
conditions using “Filter rows/Filter rows based on valid
values.” It is a best practice to use the default values “Percentage 70” in at least one group (see Note 39).
8. It is a good practice to check the correlation between column
using “Basic/Column correlations” and then applied the
“Hierarchical clustering” without clustering calculation (deselect “Rows tree” and “Columns tree” buttons). Press the multicolored wheel to see the correlation scale.
9. Replace missing LFQ values using “Imputation/Replace missing values from normal distribution” using default parameters.
(Note: Default parameters are mainly useful; however, you can
modify the width and Down shift to impute values for example
at lower intensities.)
10. Check imputation using “Histogram” button and in the histogram window select “selection from imputation” to see in red
imputed values on the blue distribution.
11. Check the relative enrichment between the two groups (e.g.,
Control versus Pulldown) using the “Volcano plot” button.
Select as the first group the Pulldown group and as the second
group the Control group. Press “OK.” Keep others parameters
by default. You can play with the FDR and the s0 values to
check different settings. When you are satisfied of your settings, save the Volcano plot using the “PDF” button (Fig. 4b)
(see Notes 40 and 41).
12. Calculate the relative protein enrichment between the two
groups using “Test/Two samples tests.” Select the same first
and second groups as previously (e.g., respectively Control and
34
Avais M. Daulat et al.
Pulldown). Set the s0 and the FDR values defined at the
previous step and press “OK.” Additional columns will be
added in the new matrix (see Note 41).
13. The new matrix contains two supplemental categorical columns and four supplemental numerical columns. The two
categorical columns are named “Student’s t-test Significant
Control_Pulldown” and “Student’s T-test Significant” containing respectively a “+” and “Control_Pulldown” to spot
significant differences. The four numerical columns are
named “Student’s T-test p-value Control_Pulldown”, “Student’s T-test q-value Control_Pulldown”, “Student’s T-test
Difference Control_Pulldown”, and “Student’s t-test Test statistic Control_Pulldown”. These columns show the P value,
the difference and the pFDR-threshold of the t test; and give
the relative enrichment of proteins between Control and Pulldown groups in a log2 scale.
14. Remove imputed value using “Imputation/Replace imputed
values by NaN” and export the resulting matrix to a text file
using the disk-shaped button (e.g., generic matrix export) (see
Note 42).
3.5
Results
3.5.1 Identification
of TANC1 Associated
Protein Complexes
Using the above described method, we purified the protein complexes associated to FLAG-TANC1 and identified bound proteins
by MS analysis (Fig. 3b). We mutated the last three amino-acids
located at the carboxy-terminus of the TANC1 sequence (SNV
changed to AAA) to abrogate PDZ interactions. Using this
approach, we were able to differentially identify proteins specifically
associated through the PDZBM of TANC1. Among the proteins
identified, we found 5 proteins containing PDZ domains (SCRIB,
DLG1, MMP7, CASK, SNX27) which were absent from the purified complex associated to the mutant form of TANC1 (TANC1AAA). Moreover, we also noticed that 41 FLAG-TANC1 partners
were also absent from the FLAG-TANC1-AAA protein complex
suggesting that association of these proteins with TANC1 relies on
the PDZBM.
We also carried out a peptide-based purification using a
TANC1 peptide ( LTAAKPKRSFIESNV ) and its mutant form
lacking the SNV motif to precipitate partners from HEK293T cell
extracts and we identified 25 PDZ domain containing proteins and
14 proteins lacking a PDZ domain including 2 proteins (CTNNA
and CPVL) found with FLAG-TANC1 (Fig. 4b).
Data obtained by the two approaches confirmed that 6 PDZ
domain proteins (SCRIB, DLG1, MMP7, CASK, PTPN13,
SNX27) (Figs. 3b and 4b) are direct interactors of the TANC1
PDZBM. However, using the peptide-based purification strategy,
we identified an additional set of 19 additional PDZ proteins
(Fig. 4b) demonstrating the higher efficiency of this technique to
identify PDZ interactions.
Identification of Associated PDZ Proteins
2
+
3
FLAG-Tanc1 (-AAA)
IP
IP: α-FLAG
WB: α-SCRIB
IP: α-FLAG
WB: α-FLAG
Lysate
WB: α-SCRIB
1
1
B)
C
FLAG-Tanc1
TA
N
-
C
tl
+
at
e
-
Ly
s
A)
35
α-SCRIB
α-TANC1
Fig. 5 Validation of the interaction between TANC1 and SCRIB. (a) HEK293T cells
expressing either wild type TANC1 or its mutant form. Following FLAG immunoprecipitation, presence of SCRIB in immunoprecipitates is validated by western
blot analysis. TANC1 is associated to SCRIB through its last three amino acids.
(b) Validation of the TANC1-SCRIB association at the endogenous level. TANC1 is
immunopurified using specific antibodies (Bethyl) and the presence of SCRIB
(antibody from Santa Cruz Biotechnology) is shown by western blot analysis
3.5.2 Validation
of the Newly Identified
TANC1-Associated PDZ
Proteins
Among the proteins associated to TANC1 by immunoprecipitation
and peptide-based purification, we found SCRIB (Figs. 3b and 4b),
a scaffold protein which regulates epithelial and neuronal functions
[13]. We first confirmed by co-immunoprecipitation followed by
western blot analysis that endogenous SCRIB is associated to
TANC1 through its PDZBM (Fig. 5a). To do so, we transfected
HEK293T cells with plasmids encoding either full length FLAGTANC1 or its mutant version (FLAG-TANC1-AAA). We observed
that endogenous SCRIB co-immunoprecipitate with full length
TANC1 but not with the mutant form. These results validate our
MS analysis and confirm that the SCRIB-TANC1 association relies
on the PDZBM of TANC1. Furthermore, to confirm that this
interaction exists at the endogenous level, we immunoprecipitated
endogenous TANC1 using a specific antibody and checked for the
presence of SCRIB (Fig. 5b). We confirmed by western blot analysis
that SCRIB is associated with TANC1 at the endogenous level.
3.6
In this chapter, we used immuno- and peptide-based purification
protocols to identify PDZ domain proteins associated to TANC1, a
protein containing a PDZBM. Our results show that the combined
approaches identified a set of 5 PDZ domain proteins which gives
high confidence in the obtained results. The peptide-based
approach was more efficient as it was able to identify four times
more PDZ domain proteins associated to TANC1. Immunopurification of TANC1 using the FLAG sequence allows the purification
of interactors of an entire and structurally folded protein. Furthermore, the given protein of interest (here TANC1) is expressed in its
natural subcellular localization (cytosol, plasma membrane. . .) and
has access to a limited number of interactors. Peptide-based purification relies on peptide-coated beads which provide more binding
Discussion
36
Avais M. Daulat et al.
sites for interaction with extracted proteins and is thus more efficient in terms of purification. However, peptides have access to the
whole set of extracted proteins regardless of their localization in
cells and thus detect “non-natural” interactions. A big advantage of
the peptide pulldown is that it tells precisely where the partners
bind within the bait (here to its carboxy-terminal sequence). We
recently used this combined approach with great success for numerous proteins harboring PDZ binding motives such as JamC [14],
Fas [15], Angiomotin [16], Vangl2 [17], or Amotl2 [18]. These
approaches are versatile, purify protein complexes in native conditions, and can be used with lysates of cellular or tissue origins.
4
Notes
1. Any vectors can be used. We generally used a backbone encoding for a gene of resistance to select stable cell lines. Our vector
contains an internal ribosome entry site (IRES) encoding for
the expression of the puromycin-resistance gene required for
the establishment of stable cell lines or populations expressing
the fusion proteins.
2. The identification of potential interacting PDZ domain proteins rely on their expression in a given cell line. Depending on
the biological question, it can be suitable to use other appropriate cell lines. We generally work with HEK293T cells since
they are easy to handle and transfect.
3. We recommend using the polyethyleneimine based transfection
procedure, which is inexpensive and efficient for the transfection of HEK293T cells, however, any other transfection
reagent may be used. To avoid false positive interacting proteins and undesired heat shock proteins associating with the
bait protein, use of clones expressing lower levels of the fusion
proteins may help.
4. Overexpression of some proteins or mutant form can lead to
cytotoxicity. To overcome this limitation, we recommend the
use of inducible expression cell system such as Flp-inTM
T-RexTM system (Thermo Fisher Scientific).
5. Half of the sample can be stored in frozen vials or used to
generate cell lysates for western blot analysis of gene expression
to confirm transfection efficiency.
6. Depending on the expression level and purification yield, more
starting materiel may be necessary. For some purification, we
used up to 1 109 cells.
7. All buffers should be filtrated with 0.44 μm filters to remove
any trace of dust and keratins.
Identification of Associated PDZ Proteins
37
8. If the sample is aimed for MS analysis, extra care should be
taken to minimize keratin contamination from the surrounding
environment, and all samples, buffers, and tubes should be
handled only using gloves.
9. To avoid discrepancies between experiments, lysis buffer containing all the inhibitors can be aliquoted in 10 mL and stored
at 20 C. Aliquots are then thawed immediately before the
experiment.
10. This could represent a good stopping point as the cells can be
stored in liquid nitrogen or at 80 C for several weeks without any decrease in protein complex isolation.
11. We recommend keeping an aliquot of protein lysates from each
step to calculate the purification yield. This can be crucial for
some proteins known to be difficult to solubilize. Determining
the purification yield can help in the choice of detergent and
concentration to maximize the solubilization of your protein of
interest.
12. To avoid protein precipitation on the beads, we recommend
avoiding drying off the beads during the washing steps.
13. Peptides coupled to beads could be stored for at least 1 year in
PBS 20% ethanol at 4 C.
14. Streptavidin could be released during later steps from the beads
(boiling, etc.) and could interfere during MS analysis as a major
contaminant.
15. Longer peptides can be synthesized but price and purity can be
a limit.
16. The reaction is rapid and usually finished after 2 h incubation.
17. For longer term storage at 4 C, 20% ethanol in PBS was used.
18. The lysis step is critical and should be carefully addressed since
proteins should be extracted from cellular compartments
(nucleus, membrane, intracellular compartments) using detergents but the integrity of protein complexes should not be
altered by using too stringent conditions.
19. Volume of lysates used is dependent on the biological materials. 1 mL of 1 mg/mL protein lysate is a good starting point.
Abundance of interactors can be checked by silver staining. If
the abundance of the interactors is considered not to be sufficient, 1 mL at 5 mg/mL could be used or if needed using
larger volume in 15-mL conical tubes. Increase the volume of
beads will generate more background.
20. Preclearing with streptavidin beads is necessary to remove
biotin dependent carboxylases including acetyl-CoA carboxylase, propionyl CoA carboxylase considered as contaminants.
38
Avais M. Daulat et al.
21. After thawing cell lysates, always centrifuge at 16,000 g for
20 min to remove aggregates.
22. 96-well plate could be stored at 20 C until further use.
23. The last three steps could be repeated if blue staining remains.
24. Any other mass spectrometers system could be used but chromatographic systems in front of the mass spectrometer should
be robust and reproducible enough to allow runs alignments
and semi quantitative analysis. Mass spectrometer should be
high performant and sensitive enough to cover the analyzed
interactome. The more performant the system, the more
robust and deep the analysis.
25. MaxQuant and Perseus versions 1.6.6.0 have been used to
describe the following steps of the bioinformatics analysis.
26. For example data analysis can be performed on Intel® Xeon®
CPU ES-2620 v4 @ 2.10 GHz (2 processors) with 46.9 GB of
installed memory (RAM) and Windows Server 2016 (Microsoft), 64 bit operating system. Sufficient space on the hard disk
is important first to copy all raw files and secondly for the
output files and temporary files. For the latter files an SSD
hard disk is desirable.
27. Alternatively the load folder option can be used to download all
raw files.
28. Other quantification methods can be used for affinity proteomics; however, Label-free quantification is mainly used. For
example, isobaric labeling [19] or metabolic labeling can also
be used [20] and set the “LFQ min. ratio count” to 1.
29. The LFQ min. ratio count can be left at the default value;
however, we prefer to use 1 at this step and then if necessary
to filter proteins quantified using 1 value during the
statistical step.
30. For example, Human protein database can be downloaded as a
fasta file from Uniprot (Click on the “Proteomes” type the
organism under study for example “Homo sapiens,” record the
organism ID 9606 and click the proteome ID link
“UP000005640”). Then download the reviewed (around
20,416 entries), the unreviewed (around 54,000 entries) or
the reference proteome (around 74,416 entries).
31. This option allows for the transfer of identification to
non-identified MS features in other LC-MS runs and so
decreases the missing values.
32. The iBAQ value can be useful because it yields an approximation of the protein abundance.
Identification of Associated PDZ Proteins
39
33. If possible, locate these folders on a faster hard drive as an SSD
to save time of analysis. The combined folder will receive all the
output files of the analysis.
34. By default the parameters file is saved beside the raw files from
LC-MS.
35. The proteinGroup.txt file is located in the output folder “combined/txt/proteinGroups.txt”.
36. If one of your interactors belongs to the contaminant list of
MaxQuant, you will lose this protein. To check the list of
contaminant goes to the MaxQuant Folder and open the following fasta file “MaxQuant/bin/conf/contaminants.fasta”.
Alternatively, you can keep the annotated contaminants proteins from your dataset.
37. Set the same name to samples belonging to the same group.
38. Alternatively you can use a different percentage to decrease
imputed values in the next steps.
39. It is a good practice to avoid to destroy the Gaussian distribution to have a better statistical analysis. You can increase the
filtering step 16 to decrease imputed values. However, for
pulldown analysis, it is not always possible because imputed
values can be important in control LC-MS.
40. Alternatively, you can use the “Hawaii plot” to display two class
of permutation FDR calculation.
41. Alternatively the number of random can be increased (e.g.,
2500 to better estimate the permutation FDR threshold).
42. Some interactors with low LFQ intensities can be found not
significant because of the imputation process. It is advisable to
inspect LFQ intensities using an Excel spreadsheet and eventually to recover some proteins with no detected values in Control conditions and valid values in pulldown conditions.
Acknowledgments
J-P.B.’s laboratory is funded by La Ligue Nationale Contre le
Cancer (Label Ligue J.P.B. 2019), Institut National du Cancer,
Fondation ARC pour la Recherche sur le Cancer, and Ruban
Rose. Through the European PDZnet consortium, JPB’s lab has
received funding from the EU Horizon 2020 RIA under the Marie
Skłodowska-Curie grant agreement No. 675341. The Marseille
Proteomic platform is funded by Institut Paoli-Calmettes, IBiSA
(Infrastructures en Biologie, Santé et Agronomie) and FEDER
(Fonds Européen de Développement Régional). J-P.B. is a scholar
of Institut Universitaire de France.
40
Avais M. Daulat et al.
References
1. Rigaut G, Shevchenko A, Rutz B, Wilm M,
Mann M, Seraphin B (1999) A generic protein
purification method for protein complex characterization and proteome exploration. Nat
Biotechnol 17:1030–1032
2. Daulat AM, Maurice P, Jockers R (2009)
Recent methodological advances in the discovery of GPCR-associated protein complexes.
Trends Pharmacol Sci 30:72–78
3. Ivarsson Y (2012) Plasticity of PDZ domains in
ligand recognition and signaling. FEBS Lett
586:2638–2647
4. te Velthuis AJ, Sakalis PA, Fowler DA,
Bagowski CP (2011) Genome-wide analysis
of PDZ domain binding reveals inherent functional overlap within the PDZ interaction network. PLoS One 6:e16047
5. Nonaka H, Takei K, Umikawa M, Oshiro M,
Kuninaka K, Bayarjargal M, Asato T,
Yamashiro Y, Uechi Y, Endo S, Suzuki T, Kariya KI (2008) MINK is a Rap2 effector for
phosphorylation of the postsynaptic scaffold
protein TANC1. Biochem Biophys Res Commun 377:573–578
6. Han S, Nam J, Li Y, Kim S, Cho SH, Cho YS,
Choi SY, Choi J, Han K, Kim Y, Na M, Kim H,
Bae YC, Kim E (2010) Regulation of dendritic
spines, spatial memory, and embryonic development by the TANC family of PSD-95-interacting proteins. J Neurosci 30:15102–15112
7. Rabilloud T (1999) Silver staining of 2-D electrophoresis gels. Methods Mol Biol
112:297–305
8. Smaczniak C, Li N, Boeren S, America T, van
Dongen W, Goerdayal SS, de Vries S, Angenent GC, Kaufmann K (2012) Proteomicsbased identification of low-abundance signaling and regulatory protein complexes in native
plant tissues. Nat Protoc 7:2144–2158
9. Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass
spectrometry-based shotgun proteomics. Nat
Protoc 11:2301–2319
10. Tyanova S, Temu T, Sinitcyn P, Carlson A,
Hein MY, Geiger T, Mann M, Cox J (2016)
The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat
Methods 13:731–740
11. Tyanova S, Cox J (2018) Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Methods Mol
Biol 1711:133–148
12. Bouyssié D, Hesse AM, Mouton-Barbosa E,
Rompais M, Macron C, Carapito C, de Peredo
AG, Couté Y, Dupierris V, Burel A, Menetrey
JP, Kalaitzakis A, Poisat J, Romdhani A, BurletSchiltz O, Cianférani S, Garin J, Bruley C
(2020) Proline: an efficient and user-friendly
software suite for large-scale proteomics. Bioinformatics 36(10):3148–3155
13. Daulat AM, Puvirajesinghe TM, Camoin L,
Borg JP (2018) Mapping cellular polarity networks using mass spectrometry-based strategies. J Mol Biol 430:3545–3564
14. Cartier-Michaud A, Bailly AL, Betzi S, Shi X,
Lissitzky JC, Zarubica A, Serge A, Roche P,
Lugari A, Hamon V, Bardin F, Derviaux C,
Lembo F, Audebert S, Marchetto S,
Durand B, Borg JP, Shi N, Morelli X,
Aurrand-Lions M (2017) Genetic, structural,
and chemical insights into the dual function of
GRASP55 in germ cell Golgi remodeling and
JAM-C polarized localization during spermatogenesis. PLoS Genet 13:e1006803
15. Gagnoux-Palacios L, Awina H, Audebert S,
Rossin A, Mondin M, Borgese F, Planas-BoteyC, Mettouchi A, Borg JP, Hueber AO (2018)
Cell polarity and adherens junction formation
inhibit epithelial Fas cell death receptor signaling. J Cell Biol 217:3839–3852
16. Ernkvist M, Luna Persson N, Audebert S,
Lecine P, Sinha I, Liu M, Schlueter M,
Horowitz A, Aase K, Weide T, Borg JP,
Majumdar A, Holmgren L (2009) The
Amot/Patj/Syx signaling complex spatially
controls RhoA GTPase activity in migrating
endothelial cells. Blood 113:244–253
17. Belotti E, Polanowska J, Daulat AM,
Audebert S, Thome V, Lissitzky JC, Lembo F,
Blibek K, Omi S, Lenfant N, Gangar A,
Montcouquiol M, Santoni MJ, Sebbagh M,
Aurrand-Lions M, Angers S, Kodjabachian L,
Reboul J, Borg JP (2013) The human
PDZome: a gateway to PSD95-Disc
large-zonula occludens (PDZ)-mediated functions. Mol Cell Proteomics 12:2587–2603
18. Mojallal M, Zheng Y, Hultin S, Audebert S,
van Harn T, Johnsson P, Lenander C, Fritz N,
Mieth C, Corcoran M, Lembo F, Hallstrom M,
Hartman J, Mazure NM, Weide T, Grander D,
Borg JP, Uhlen P, Holmgren L (2014)
AmotL2 disrupts apical-basal cell polarity and
promotes tumour invasion. Nat Commun
5:4557
19. Bai Y, Markham K, Chen F, Weerasekera R,
Watts J, Horne P, Wakutani Y, Bagshaw R,
Mathews PM, Fraser PE, Westaway D, St
George-Hyslop P, Schmitt-Ulms G (2008)
The in vivo brain interactome of the amyloid
precursor protein. Mol Cell Proteomics
7:15–34
20. Hubner NC, Bird AW, Cox J, Splettstoesser B,
Bandilla P, Poser I, Hyman A, Mann M (2010)
Quantitative proteomics combined with BAC
TransgeneOmics reveals in vivo protein interactions. J Cell Biol 189:739–754
Chapter 3
Identification of PDZ Interactions by Proteomic Peptide
Phage Display
Susanne Lüchow, Gustav N. Sundell, and Ylva Ivarsson
Abstract
PSD95-Disc large-Zonula occludens (PDZ) domains are among the most abundant modular domains in
the human proteome. They typically bind short carboxy-terminal sequence motifs of their ligand proteins,
which may be transmembrane proteins such as ion channels and GPCRs, as well as soluble proteins. The
identity of the endogenous ligands of many PDZ domains remains unclear despite more than two decades
of PDZ research. Combinatorial peptide phage display and bioinformatics predictions have contributed to
shed light on PDZ-mediated interactions. However, the efficiency of these methods for the identification of
interactions of potential biological relevance is hampered by different biases. Proteomic peptide-phage
display (ProP-PD) was developed to overcome these limitations. Here we describe a ProP-PD protocol for
the identification of C-terminal PDZ domain ligands. The method efficiently identifies peptide ligands
within a proteome of interest, and pinpoint targets of potential biological relevance.
Key words Phage display, PDZ domain, NGS, Specificity
1
Introduction
PDZ domains are abundant modular domains that are well-known
for binding to C-terminal binding motifs. The three main classes of
PDZ binding motifs are class I [T/S]-X-Φ-COO-, class II
Φ-X-Φ-COO-, and class III [D/E]-X-Φ-COO-, where X stands
for any amino acid and Φ stands for a hydrophobic amino acid
[1, 2]. The ligand binding specificities of a large portion of human
PDZ domains have been charted through combinatorial peptide
phage display [3], in which a highly diverse peptide phage display
library was used to identify high affinity ligands. The analysis
revealed that the specificities of PDZ domains may be expanded
and subdivided. The information has been used for large-scale
predictions of PDZ mediated interactions in the proteome [4],
and structures of representative cases of high-affinity interactions
have provided important insights into the binding determinant of
PDZ domain interactions [5]. The high-affinity binders generated
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021
41
42
Susanne Lüchow et al.
through combinatorial peptide phage display can also be valuable as
starting points for inhibitor design [6]. However, the consensus
binding motifs that are derived based on these results have been
found to be overly hydrophobic and tryptophan rich, which limits
the usability of such information for predicting interactions in a
given proteome [7]. We therefore developed a variant of phage
display termed proteomic peptide-phage display (ProP-PD) where
the displayed peptides are designed to encompass selected regions
of a given proteome [8]. The method is based on a combination of
computational library design, oligonucleotide library synthesis,
phage display, and next-generation sequencing (NGS). We have
shown that the approach can be used to successfully identify endogenous PDZ mediated interactions [9] and can be geared for the
identification of interactions that are tuned by ligand phosphorylation [10]. In this chapter we describe a comprehensive ProP-PD
protocol including (a) library construction, (b) ProP-PD selections
against PDZ domains, and (c) NGS of the peptide coding regions
of binding enriched phage pools (Fig. 1).
2
Materials
2.1 Oligonucleotide
Pool Amplification
1. Phusion high-fidelity PCR master mix with HF buffer.
2. Custom designed oligonucleotide pool from commercial
provider.
3. Agarose (molecular grade).
4. GelRed.
5. Nucleotide Removal Kit.
6. Quant-iT PicoGreen dsDNA assay kit.
7. 10 TE buffer: 100 mM Tris–HCl, 10 mM EDTA, pH 8.0
8. Lambda phage dsDNA (100 μg/ml dsDNA).
9. White 96-well hard-shell PCR plates, thin wall.
10. CDF96 Real-Time system.
2.2 Purification
of dU-ssDNA
Phagemid
1. E. coli CJ360 cells.
2. M13K07 helper phage.
3. LB/carb agar plates: 1% tryptone (w/v), 1% NaCl (w/v), 0.5%
yeast extract (w/v), 1.5% agar (w/v), 100 μg/ml carbenicillin.
4. 2YT: 1.6% tryptone (w/v), 0.5% NaCl (w/v), 1% yeast extract
(w/v)
5. 100 mg/ml carbenicillin dissolved in deionized water. Sterile
filter.
6. 34 mg/ml chloramphenicol dissolved in 70 v/v % ethanol.
Sterile filter.
Proteomic Peptide-Phage Display of PDZ Domains
43
Fig. 1 Schematic overview of the ProP-PD protocol. (a) Phage library construction. 1. Design bioinformatically
a library that tile regions of interest and obtain the designed oligonucleotide from a commercial provider. 2.
Generate ssDNA of a suitable phagemid. PCR amplify the oligonucleotide pool and phosphorylate the 50 end of
the oligos. 4. Anneal the oligos to the ssDNA. 5. Second strand synthesis and ligation. 6. Electroporate the
library into SS320 E. coli cells preinfected with M13KO7 helper phage. Amplify the phage library overnight. 7.
Purify the ProP-PD library. (b) Protein expression and purification. 1. Express the bait protein in 50 ml culture.
2. Batch purify. (c) Phage display selection. 1. Immobilize the bait protein and incubate it with the phage
library. 2. Wash away unbound phage. 3. Elute bound phage by E coli infection. 4. Amplify the enriched phage
pool and use them as in-phages for the next round of selection. (d) Next-generation sequencing (NGS). 1. PCR
amplify the peptide coding regions from the binding enriched phage pools, and add adaptors and barcode for
the analysis. 2. Purify and normalize the PCR products using magnetic beads. 3. Pool the purified DNA and
quantify it using PicoGreen. 4. Sequence the sample using Illumina MySeq, which will generate over 18 million
reads. Demultiplex the sequences and map them to the library design
44
Susanne Lüchow et al.
7. 25 mg/ml kanamycin dissolved in deionized water. Sterile
filter.
8. 2YT/carb/kan/uridine: 2YT, 100 μg/ml carbenicillin, 25 μg/
ml kanamycin, 0.25 μg/ml uridine
9. PEG/NaCl: 20% PEG-8000 (w/v), 2.5 M NaCl (w/v). Sterile
filter.
10. PBS: 137 mM NaCl, 3 mM KCl, 8 mM Na2HPO4, 1.5 mM
KH2PO4. Adjust pH to 7.4 and autoclave.
11. QIAprep Spin M13 Kit.
12. MLB buffer: 1 M sodium perchlorate, 30% isopropanol (v/v).
13. Elution Buffer EB: 10 mM Tris–HCl, pH 8.5. Sterile filter.
14. 10 TBE Buffer: 1 M Tris base, 1 M boric acid,
0.02 M EDTA.
2.3 In Vitro Synthesis
of Phagemid dsDNA
Library
1. Exonuclease I (ExoI).
2. 10 TM buffer: 0.1 M MgCl2, 0.5 M Tris, pH 7.5.
3. 10 mM ATP, dissolve in H2O and sterile filter.
4. 100 mM dithiothreitol (DTT), dissolve in H2O and sterile
filter.
5. T4 polynucleotide kinase.
6. 10 mM dNTP mix
7. T7 DNA polymerase.
8. T4 DNA ligase.
9. FastDigest SmaI.
10. Qiagen QIA quick DNA purification kit.
11. QG buffer: 5.5 M guanidine thiocyanate, 20 mM Tris–HCl,
pH 6.6.
12. PE buffer: 10 mM Tris–HCl, 80% ethanol (v/v), pH 7.5.
13. Ultrapure water.
14. Agarose.
15. GelRed.
16. 10 TBE Buffer.
2.4 Electroporation
and Amplification
of Library
1. E. coli SS320 preinfected with M13K07 helper phage prepared
as described elsewhere [11].
2. Gene Pulser electroporation device.
3. 0.1 cm gap electrode
4. SOC media: 0.5% yeast extract (w/v), 2% tryptone (w/v), 0.5%
NaCl (w/v), 0.2% KCl (w/v). Adjust pH to 7.0 with NaOH,
after autoclavation. Add 5.0 ml of autoclaved 2.0 M MgCl2 and
20 ml of filter-sterilized 1.0 M glucose.
Proteomic Peptide-Phage Display of PDZ Domains
45
5. LB/carb plate (see item 3, Subheading 2.2).
6. PEG/NaCl (see item 9, Subheading 2.2).
7. PBT buffer: PBS, 0.05% Tween 20 (v/v), 0.2% BSA (w/v).
Sterile filter.
8. Ultrapure glycerol.
9. OMNImax E. coli.
2.5 Protein
Expression
and Purification
1. 2YT (see item 4, Subheading 2.2)
2. 1 M isopropyl β-D-1-thiogalactopyranoside (IPTG). Dissolve
in H2O, sterile filter.
3. Lysis buffer: PBS, 1% Triton X-100 (v/v), 1.6 mM MgCl2,
0.1 μg/ml lysozyme, 15 μg/ml DNase I, 1 mM PMSF.
4. Wash buffer: PBS, 30 mM imidazole, pH 7.5.
5. Elution buffer: PBS, 300 mM imidazole, 5% glycerol, pH 7.5.
6. 100 mM EDTA in PBS.
7. Ni-NTA Agarose.
2.6
Phage Selections
1. PBS.
2. Blocking solution: PBS, 0.5% BSA (w/v). Sterile filter.
3. PT buffer: PBS, 0.05% Tween 20 (v/v). Sterile filter.
4. PBT buffer (as described in item 7, Subheading 2.4).
5. PEG/NaCl (see item 9, Subheading 2.2).
6. 2YT (see item 4, Subheading 2.2)
7. OMNImax E. coli.
8. 1 1011 p.f.u./ml M13KO7 helper phage
9. 10 mg/ml tetracycline dissolved in 50% ethanol. Filter sterile
and protect from light.
10. 100 μg/ml carbenicillin
11. 30 μg/ml kanamycin.
2.7 Phage Pool
Enzyme Linked
Immunosorbent Assay
(ELISA)
1. PBS.
2. Blocking solution (see item 2, Subheading 2.6).
3. PT buffer (see item 3, Subheading 2.6).
4. PBT (see item 4, Subheading 2.6).
5. HRP-conjugated M13 antibody.
6. 3,30 ,5,50 -tetramethylbenzidine (TMB) substrate kit: TMB
solution, peroxide solution
7. 0.6 M H2SO4.
46
Susanne Lüchow et al.
2.8 Preparation
and Quantification
of Sample
for Next-Generation
Sequencing (NGS)
1. Phusion high fidelity PCR master mix with HF buffer.
2. NGS grade PCR primers with barcodes.
3. Mag-Bind total Pure NGS.
4. EB Buffer: provided by the kit manufacturer.
5. 10 TE buffer
6. QG buffer.
7. 70% ethanol
8. GelRed.
9. Agarose.
10. 10 TBE
11. Quant-iT PicoGreen dsDNA assay kit.
3
Methods
3.1 PCR
Amplification of a
Commercial Custom
Oligo Pools
Custom designed oligonucleotide libraries can be obtained from
commercial providers. The details of the design of the oligonucleotide libraries will depend on the research questions, which is outside
the scope of this protocol. In general terms, we retrieve the peptide
sequences of interest from a reliable source (e.g., Uniprot),
reversely translate the regions of interest, and add on flanking
oligonucleotide stretches that are complementary to the phagemid
vector. For the C-terminal display on the M13 phage we construct
the library using a phagemid that encodes an engineered version of
the major coat protein P8 engineered by the Sidhu lab [12]. To
remove 50 and 30 adaptor sequences we typically amplify the
obtained oligonucleotide library before use through the following
steps.
1. Prepare 4–8 PCR reactions mixing 5 μl of 5 μM forward and
reverse primers each, 25 μl of Phusion high fidelity PCR master
mix with Phusion HF buffer, 1 μl of the custom oligonucleotide pool and 14 μl H2O.
2. Run a PCR for 18 cycles with denaturation 15 s at 98 C,
annealing at 56 C for 15 s, and elongation at 72 C for 10 s.
A rather low number of PCR cycles (18–20) is used to reduce
the PCR bias.
3. Verify the product by electrophoresis, using a 2% agarose gel
with GelRed staining and loading 1 μl of each PCR reaction.
4. Pool the four best reactions and clean them up using Nucleotide Removal Kit. Elute in 30 μl of elution buffer. Note that the
PCR primers are most likely not removed by this, but they will
be removed at a later stage.
Proteomic Peptide-Phage Display of PDZ Domains
3.2 Quantification
of the PCR Product
(See Note 1)
47
1. Dilute 10 TE buffer in MilliQ water to 1.
2. Dilute PicoGreen dye 1:400 in TE buffer.
3. Prepare duplicate of 8 steps two-times serial dilution of lambda
phage dsDNA standard stock (100 μg/ml dsDNA).
4. Add 25 μl of the dye to 16 wells in a 96-well plate compatible
with a qPCR instrument (e.g., white well hard-shell PCR plates
96-well, thin wall). Add 1 μl of the serial dilution to each well.
5. Prepare 1:2, 1:4, and 1:8 dilutions of your PCR reaction.
6. Mix 1 μl of the dilution with 25 μl dye in separate wells.
7. Spin the plates at 100 g to ensure that the solution is at the
bottom of the wells.
8. Run a single cycle 20 C 1 min qPCR with a readout of SYBR or
fluorescein settings: excitation ~480 nm, emission 520 nm.
9. Generate a standard curve using the duplicate dilutions that are
in the linear range.
10. Determine the DNA concentration of the diluted PCR reaction from the standard curve.
3.3 Purification
of dU-ssDNA
Phagemid
This protocol will yield dU-ssDNA in amounts exceeding 20 μg,
which is enough for production of 2-4 ProP-PD libraries depending on library diversity, using a modified version of the QIAprep
spin kit described previously [11].
1. Transform your phagemid into CJ360 (or alternative dut/
ung strain) and plate it on an LB/carb plate. Incubate overnight at 37 C.
2. Inoculate a single CJ360 colony into 1 ml of 2YT medium
supplemented with M13KO7 helper phage (1010 pfu/ml),
100 μg/ml carbenicillin (final concentration) and 34 μg/ml
chloramphenicol to maintain the phagemid and the epitope for
the F0 episome of E. coli CJ236. Incubate the culture for 2 h in
37 C with shaking at 200 rpm (see Note 2).
3. Add 25 μg/ml kanamycin (final concentration) to select for
bacteria coinfected with M13KO7 helper phage. Shake at
200 rpm for 6 h at 37 C.
4. Transfer the bacterial culture into 30 ml 2YT/carb/kan/uridine medium and incubate at 37 C for 20 h with shaking at
200 rpm.
5. Pellet the bacteria by centrifugation for 10 min at 12,000 g
using a Beckman JA14.50 rotor or equivalent. Transfer the
supernatant into a new tube containing 1/5 the final volume
of PEG/NaCl. Incubate on ice for 5 min.
48
Susanne Lüchow et al.
6. Centrifuge the sample for 10 min at 12,000 g to pellet the
precipitated phages. Decant the supernatant, and spin briefly
for 2 min at 2000 g to concentrate the phage pellet. Aspirate
the remaining supernatant.
7. Resuspend the phage pellet in 500 μl PBS and transfer it to a
microcentrifuge tube and centrifuge it at 14,000 g for 5 min
to remove insoluble debris and transfer the supernatant to a
new microcentrifuge tube.
8. Add 7 μl buffer MP, mix and incubate at room temperature for
2 min.
9. Add the sample to a spin column in a 2 ml microcentrifuge
tube, centrifuge at 6000 g for 30 s and discard the flowthrough. The phage remains bound to the spin column matrix.
10. Add 0.7 ml MLB buffer to the spin column and centrifuge at
6000 g for 30 s and discard the flow through.
11. Add 0.7 ml MLB buffer to the spin column, incubate for 1 min
and centrifuge at 6000 g for 30 s and discard the flow
through. The DNA is now bound to the column matrix.
12. Add 0.7 ml PE buffer to the spin column and centrifuge at
6000 g for 30 s and discard the flow through.
13. Repeat step 12.
14. Centrifuge the empty tube at 6000 g for 30 s to remove
residual buffer.
15. Transfer the column to a fresh 1.5 ml microcentrifuge tube and
add 100 μl buffer EB to the center of the membrane.
16. Incubate for 10 min at room temperature and then centrifuge
at 6000 g for 30 s. The eluent contains the purified
dU-ssDNA.
17. Analyze 1 μl of the DNA by 1% (w/v) agarose gel electrophoresis using 1xTBE buffer and 1:10,000 GelRed.
18. Determine the DNA concentration by absorbance at 260 nm
(A260 ¼ 1.0 for 33 ng/ml of ssDNA) using a NanoDrop or
equivalent.
3.4 In Vitro Synthesis
of the Phagemid
dsDNA Library
In this process you will turn the amplified oligonucleotide pool and
the dU-ssDNA in to a heteroduplex covalently closed circular
double stranded DNA (CCC-dsDNA). This will be done in three
steps: 50 phosphorylation of the PCR amplified oligonucleotide
pool, annealing to the circular dU-ssDNA and enzymatic synthesis
of the second strand to create CCC-dsDNA. This is a scaled-up
version of a published method by Kunkel and coworkers [13], and
modified for the use of PCR amplified oligonucleotide library.
Before phosphorylating the PCR amplified oligonucleotide pool,
you will remove the remaining single stranded PCR primers by
treating the sample with ExoI.
Proteomic Peptide-Phage Display of PDZ Domains
49
1. Remove residual ssDNA by ExoI treatment of 0.3 μg of the
PCR amplified oligonucleotide library using 0.2 units/μl,
37 C for 30 min, 85 C for 15 min, followed by flash cooling
on ice.
2. 5’Phosphorylate the DNA directly after flash cooling. Combine in a 1.5 ml micro centrifuge tube the library with 2 μl 10
TM buffer, 2 μl 10 mM ATP, and 1 μl 100 mM DTT. Add
water up to 20 μl.
3. Add 10 units of T4 polynucleotide kinase and incubate for 1 h
at 37 C. Use the phosphorylated sample directly for annealing.
4. To 10 μg of dU-ssDNA add 25 μl of 10 TM buffer, 20 μl of
the phosphorylated oligonucleotides and adjust to a final volume of 250 μl.
5. Incubate at 90 C for 3 min, 50 C for 3 min and 20 C for
5 min.
6. Add 10 μl of 10 mM dNTP, 10 μl 10 mM ATP 15 μl 100 mM
DTT, 30 units T7 DNA polymerase, and 30 Weiss units of T4
DNA ligase to the annealed oligonucleotides.
7. Incubate the reaction for 16 h at 20 C. This will synthesize the
second strand and ligate it to form the heteroduplex
CCC-dsDNA.
8. Freeze-thaw the sample 3 times to inactivate the enzymes.
9. Add 5 μl Fast-digest SmaI to remove phagemid without insert
(see Note 3). Incubate for 30 min at 37 C.
10. Purify and desalt the CCC-dsDNA using QIAquick DNA
purification kit. Add 1 ml of QG buffer to the reaction and mix.
11. Load the sample to two spin columns placed in 2-ml microcentrifuge tubes. Centrifuge at 16,000 g for 1 min, discard
the flow through.
12. To each column add 750 μl of PE buffer and spin the sample at
16,000 g for 1 min, discard the flow through.
13. Centrifuge the columns an extra time at 16,000 g for 1 min
to remove excess PE buffer.
14. Transfer the column to a new 1.5 ml microcentrifuge tube, add
35 μl of ultrapure water to each membrane. Incubate at room
temperature for 2 min.
15. Spin the column at 11,000 g for 1 min and combine the
eluent from both columns in to one tube.
16. Analyze the purified dsDNA alongside the template using
DNA electrophoresis gel (1% (w/v) agarose) to estimate how
much of the dU-ssDNA has been converted in to
CCC-dsDNA. Load 1 μl of the reaction next to 1 μl of
dU-ssDNA. The CCC-dsDNA might show several bands:
50
Susanne Lüchow et al.
one at the size of the ssDNA which is DNA that has not been
converted, another at the size of the CCC-dsDNA product
correctly extended and ligated, and possibly a band with unligated dsDNA higher up on the gel.
17. The desalted CCC-dsDNA can directly be used for electroporation to create a phage library, or frozen for later use.
3.5 Electroporation
of Phagemid Library
and Amplification
of Phage Library
1. Chill 100 μl CCC-dsDNA and a 0.1 cm gap electrode on ice.
2. Thaw an aliquot of electrocompetent E. coli SS320 preinfected
with M13K07 on ice. Add 150 μl of the bacteria to the DNA
and mix carefully to avoid creating bubbles.
3. Transfer the mixture to the prechilled cuvette and electroporate the mixture with a Gene Pulser with the settings 1.8 kV
200 Ω and 25 μF. Take note of the time constant. A time
constant in the range of 3.5–4 is typically indicative of a successful transformation.
4. Immediately add 1 ml prewarmed SOC media to the cuvette
and transfer the culture to 10 ml SOC media in a 250 ml flask.
Rinse the cuvette with SOC media two times (pipet up and
down a couple of times to mix) and add it to the culture. Add
additional SOC medium to a final volume of 25 ml.
5. Incubate for 30 min at 37 C with shaking at 200 rpm.
6. To determine the efficiency of the electroporation, make three
10 serial dilutions, for 12 dilution steps of the 25 ml culture,
exactly 30 min after electroporation. Spot 5 μl of each serial
dilution on an LB/carb plate and incubate at 37 C overnight.
Count the number of colonies and calculate backwards to
determine the number of colony-forming units in the 25 ml
SOC media. This sets the upper limit of the library size. When
making ProP-PD libraries, the aim is to have at least 100-1000
times more transformants than the number of individual
sequences in the library design to ensure a comprehensive
coverage.
7. Transfer the culture to a 2 l flask containing 500 ml 2YT/carbenicillin/kanamycin medium and incubate it for at least 16 h
at 37 C shaking and 200 rpm.
8. Centrifuge the culture at 16,000 g for 10 min in a Beckman
JA14 rotor or equivalent.
9. Transfer the supernatant containing the phage library to a new
centrifuge tube containing 1/5 of the final volume of PEG/NaCl solution for phage precipitation.
10. Incubate 5 min at room temperature and spin it at 16,000 g
for 10 min. Remove the supernatant.
Proteomic Peptide-Phage Display of PDZ Domains
51
11. Centrifuge for 5 min at 10,000 g to concentrate the pellet.
Aspirate the remaining liquid.
12. Resuspend the pellet in 20 ml of PBT buffer and transfer to a
50 ml conical tube and centrifuge for 5 min at 16,000 g to
pellet insoluble debris.
13. Transfer the supernatant to a new tube. Estimate the concentration by A268 (OD268 ¼ 1 corresponds to approximately
5 1012 phage/ml). The concentration of the library can be
better estimated by making a 10 dilution series of the library,
infecting log phase OMNImax E. coli and plating on LB/carb
plates.
14. Add ultrapure glycerol to a final concentration of 10% and store
the library at 80 C (see Note 4). The quality and coverage of
the constructed library should be analyzed through NGS as
described in Subheading 3.8.
3.6 Bait Protein
Expression
and Purification
We typically use GST- or MBP-tagged PDZ domains, and remove
nonspecific binding phages and potential GST/MBP binders
through preselections against immobilized GST/MBP. For ProPPD, 10 μg protein is needed per selection day and at least half the
amount is needed for pooled phage ELISA. For 4 days of selection,
the minimum amount of protein needed is 60 μg. However, we
recommend performing triplicate selections, and to purify
0.5–1 mg protein of at least 90% purity. Below is a protocol for
batch purification of His-GST/MBP-tagged PDZ domains using
Ni-NTA beads starting from the expression construct in a pETM
vector.
3.6.1 Protein Expression
1. Transform the expression plasmid into E. coli BL21 (DE3).
2. Pick a single colony in to a 5 ml 2YT culture and grow it
overnight with shaking.
3. Transfer 1 ml of overnight culture into a 250 ml flask with
50 ml 2YT media supplemented with appropriate antibiotic.
4. Incubate at 37 C for approximately 2–3 h with shaking.
5. Measure OD600.
6. Induce protein expression at OD600 ¼ 0.6-0.8 with 1 mM
IPTG final concentration.
7. Lower the temperature to 30 C and allow protein expression
to proceed for 4 hours.
8. Centrifuge cultures at 4 C for 20 min at 3000 g. Discard the
supernatant.
9. Freeze the bacterial pellet at 20 C.
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Susanne Lüchow et al.
3.6.2 Batch Purification
1. Resuspend pellet in 5 ml cold lysis buffer (lysozyme, DNase1,
and PMSF is added immediately before use).
2. Incubate for 1 h at 4 C under gentle agitation.
3. Pellet insoluble cell debris by centrifugation for 1 h at
17,000 g at 4 C.
4. Prepare precharged Ni-NTA agarose beads (650 μl/sample) by
washing three times:
(a) Transfer Ni-beads into 15 ml centrifuge tube.
(b) Fill the tube with H2O.
(c) Centrifuge for 5 min at 500 g at room temperature.
(d) Carefully discard the liquid.
(e) Repeat steps b–d two more times.
(f) Equilibrate the beads with wash buffer by resuspending
them in 10 ml buffer. Centrifuge for 5 min at 500 g and
remove the buffer.
5. Add the cleared lysate (from step 3) to the Ni-NTA agarose
beads. Incubate for 1 h at 4 C under gentle agitation.
6. Centrifuge for 5 min at 500 g and 4 C.
7. Remove the supernatant, and add 40 ml cold wash buffer. Turn
the tube gently upside down to resuspend the beads.
8. Repeat steps 6 and 7, then centrifuge again 5 min at 500 g
and 4 C.
9. Remove the supernatant without disturbing the pelleted beads.
10. Resuspend the beads in 1.2 ml elution buffer.
11. Centrifuge for 5 min at 500 g at 4 C.
12. Transfer the first elution into a clean 1.5 ml tube and keep
on ice.
13. Add 1.2 ml elution buffer to Ni-beads.
14. Centrifuge for 5 min at 500 g and 4 C.
15. Transfer the second elution into a new tube.
16. Confirm the size and the purity of the protein using
SDS-PAGE.
17. Estimate the concentration using a NanoDrop, or determine
the concentration more accurately using a spectrophotometer
and using the correct extinction coefficient.
18. Store the protein for short term at 4 C or for longer term at
20 C. For long-term storage, add 15% glycerol and flashfreeze.
Proteomic Peptide-Phage Display of PDZ Domains
53
3.7 ProP-PD
Selection Against PDZ
Domains
The P8 protein used for C-terminal peptide phage display is permutated as compared to the wild-type p8 protein [12]. This makes
the display system less suited for high-throughput selections
described elsewhere [14], and we therefore follow a lower throughput protocol for C-terminal ProP-PD against PDZ domains. There
is typically a tenfold enrichment of binders per day of selection, and
we recommend to perform at least four consecutive days of selections, and to evaluate the success of the selection through pooled
phage ELISA the fifth day, although it is possible to delay the
pooled phage ELISA to the following week.
3.7.1 Day 0
1. Coat a 96-well MaxiSorp plate with purified bait protein (one
well per replicate selection. To each well, add 10 μg GST/
MBP-tagged PDZ domain in 100 μl PBS. In parallel, coat a
negative control plate with 10 μg GST/MBP. One well of
immobilized bait protein per replicate is typically enough for
selection against ProP-PD libraries (diversity in the order of
10,000 to 1 106). As a rule of thumb, the number of phages
used should surpass 1000 times the library diversity, and the
phage concentration should not be more than 1013 phage/ml.
2. Cover the plates with plastic seals and incubate overnight at
4 C with shaking. Note that immobilization also can be performed for 2 h at room temperature if needed.
3. Start an overnight culture of E. coli OmniMAX: Inoculate a
single colony E. coli OmniMAX in 10 ml 2YT supplemented
with 10 μl 10 mg/ml tetracycline. Incubate overnight at 37 C,
200 rpm. Store the stock culture at 4 C for the duration of the
phage selection protocol.
3.7.2 Selection Day 1
1. Inoculate 3 10 ml 2YT in 50 ml Falcon tubes with 20 μl of
the stock E. coli OmniMAX culture. Culture 1 should be
supplemented with 10 μl tetracycline, and will be used for
elution of bound phages, Culture 2 and culture 3 should be
supplemented with 10 μl carbenicillin or 10 μl kanamycin, for
control of preinfection of library phage or M13KO7 helper
phage, respectively. Incubate the cultures at 37 C with
shaking.
2. Remove the protein solutions from the MaxiSorp plates and
block each well with 200 μl blocking solution for 1 h at 4 C.
3. Prepare naı̈ve phage library from glycerol stocks: Calculate the
amount of library needed for the selection (1000 library
diversity number of wells). Withdraw the required amount
of phage library from the stock solution, and dilute it 10 times
in PBS. Precipitate the library by the addition of 1/5 final
volume of PEG/NaCl. Incubate for 10 min on ice. Centrifuge
for 10 min at 17,000 g. Remove the supernatant by pouring.
54
Susanne Lüchow et al.
Spin for 2 min at 4000 g and aspirate the rest of the liquid.
Resuspend the phage pellet in required volume of PBT
(100 μl/well of bait proteins).
4. Wash the preselection plate four times with 200 μl cold PT
buffer and add 100 μl phage library to each well. Incubate for
1 h at 4 C with shaking.
5. Wash the target-coated plates four times with 200 μl cold PT
buffer and transfer the phage solution from the preselection
plate to the target-coated plate. Allow the phages to bind to the
bait proteins for 2 h at 4 C with shaking.
6. Measure the OD600 for the E. coli OmniMAX culture supplemented with tetracycline. The OD600 should be 0.6–0.8 at the
time of phage elution. If necessary, dilute the culture with 2YT
medium and allow to bacteria to grow to desired OD600.
Ensure that there is no bacterial growth in the E. coli OmniMAX cultures supplemented with carbenicillin or kanamycin
(see Note 5).
7. Remove the unbound phage solution from the bait proteins
and wash the wells 5 times with 200 μl cold PT buffer.
8. Elute bound phage by adding 100 μl of actively growing
(OD600 0.6-0.8) E. coli OmniMAX to each well. Cover the
plate with a gas-permeable film and incubate for 30 min at
37 C at 200 rpm.
9. Titrate the number of in-phages during the incubation time:
Make dilution series for 10 μl of in-phages using the log phase
E. coli OmniMAX culture. Add 90 μl culture to each well of a
96-well PCR plate and transfer 10 μl in-phages in first row of
wells, mix, then transfer 10 μl to next well and so on. Change
pipette tips between wells. Spot 5 μl of each dilution on
LB/carb plates. Incubate plates overnight at 37 C.
10. Titer the number of out-phages after incubation: Sample 10 μl
of eluted phages for dilution series. Prepare dilution series as
described above, but using 2YT media. Incubate plates overnight at 37 C.
11. Add 10 μl of M13KO7 helper phage (1 1011 p.f.u./ml) to
each well. Incubate for 45 min at 37 C at 200 rpm.
12. Transfer the phage infected E. coli OmniMAX culture of each
well to 10 ml 2YT supplemented with 10 μl of carbenicillin and
kanamycin, and 0.3 mM IPTG in 50 ml conical tubes. Incubate
overnight at 37 C with shaking at 200 rpm.
13. Coat the desired number of wells of a 96-well MaxiSorp plate
with bait proteins (10 μl protein in 100 μl PBS) and the
matching number of wells with GST/MBP for preselection in
preparation for next round of selection (see day 1, step 1 + 2).
Incubate the MaxiSorp plates at 4 C overnight under gentle
agitation.
Proteomic Peptide-Phage Display of PDZ Domains
3.7.3 Selection Day 2–4
55
Repeat the selection steps using the out-phage form the previous
day as in-phage for the next round of selection. This procedure is
repeated for a total of four rounds.
1. Start 4 10 ml E. coli OmniMAX cultures from the ON
culture stored in 4 C (see day 1, step 1).
2. Block each well of the preselection plate and the target coated
plate with 200 μl blocking solution for 1 h at 4 C with shaking.
3. Harvest phages from previous day: Pellet the bacteria by centrifugation (15 min, 3000 g).
4. Transfer the phage supernatants to fresh Falcon tubes supplemented with 1/5 final volume PEG/NaCl. Incubate for
15 min on ice. Centrifuge for 15 min at 16,000 g. Decant
the supernatant, then spin another 3 min at 16,000 g. Aspirate the remaining liquid. Resuspend the phage pellet in 1/10
volume (1 ml) cold PBS.
5. Continue with steps 4–13 of the protocol from day 1, using
100 μl resuspended phage pool as in-phages.
6. Analyze the progress of the selection by calculating the ratio of
out-phages versus in-phages for each day of selection.
7. Day 4: Immobilize protein (5–10 μg bait protein or
GST/MBP in 100 μl buffer) for analysis of the progress of
the selection through pooled phage ELISA (Fig. 2). Cover
the plates with a clear adhesive film and incubate overnight at
4 C.
3.7.4 Day 5:
Phage pool ELISA
After the last selection day, harvest the out-phages as described
above. Save 50 μl of the out-phage pools from each day of selection
in 20 C. Perform phage pool ELISA.
1. Block each well with blocking solution for 1 h at 4 C.
2. Wash the blocked plate four times with PT buffer.
3. Add 100 μl phage supernatant from the binding selections to
the corresponding well of the target-coated plate and the
GST/MBP-coated wells. Incubate the plates for 2 h at 4 C.
4. Wash five times with PT buffer.
5. To each well, add 100 μl anti-M13 antibody-HRP conjugate
(1:5000 dilution) in PBT buffer and incubate for 1 h at 4 C.
6. Wash four times with PT buffer and one time with PBS.
Remove as much liquid as possible by tapping the plate on
paper.
7. To each well, add 100 μl TMB substrate (consists of 1:1
TMB + peroxide solution mixed immediately before use).
Allow color to develop for 1-15 min (depending on how the
color develop) and stop the reaction by adding 100 μl 0.6 M
H2SO4.
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Susanne Lüchow et al.
Fig. 2 Phage pool ELISA analysis. (a) Samples (MBP/GST-tagged bait proteins)
and their negative controls (MBP/GST) are immobilized side-by-side in a 96-well
plate, and incubated with binding enriched phage pools. Unbound phage is
washed away and the bound phage is detected by an HRP-linked M13
antibody. (b) The progress of the selection is evaluated by comparing the ratio
of the ELISA signal of the bait protein and the negative control over the days of
selection. The ratio between bait and reference signal should be at least 2 for a
selection to be considered successful. Here, bait 1 represents a successful
selection, where the ELISA signals from selection day 2, 3, and 4 suggest that
the number of phages bound to the bait protein surpasses the number bound to
the reference by several folds. Bait 2 represents an unsuccessful selection
Proteomic Peptide-Phage Display of PDZ Domains
57
Read spectrophotometrically at 450 nm in a microtiter plate
reader. Retrieve the results and visualize the ratios between the
A450target and A450control. The ratio should be higher than 2 (and
ideally more) for a selection to be considered successful.
3.8 Preparation
of Sample for NSG
Analysis
To analyze the results of the binding enriched phage pools, the
DNA sequences encoding the displayed peptides are sequenced
using NGS. To prepare phage pools for NGS, the DNA is amplified
and barcoded. Barcodes are unique stretches of nucleotides, which
identify the PCR product as coming from a specific phage pool.
Examples of barcodes and adaptors for Illumina sequencing can be
found elsewhere [15]. NGS facilities can typically provide advices
on barcoding strategies and provide support for demultiplexing (see
Note 6).
3.8.1 PCR Amplification
and Barcoding
1. Dissolve NGS grade primers (100 μM).
2. Dilute the primers to 5 μM and array them in a 96-well PCR
plate.
3. Dilute the out-phage pools 10 in 2YT in a 96-well PCR plate.
The diluted phage supernatants are used as template in the
PCR reaction.
4. Prepare PCR samples by for each reaction mixing the following: 12.5 μl 2x Phusion Master Mix, 2.5 μl forward primer
(5 μM) and 2.5 μl reverse primer, 2.5 μl phage pool template
and 5 μl dH2O. Mix primers, template and dH2O first. Ensure
that the PCR machine is 98 C, then add Phusion Master Mix,
and start the PCR directly following the program in Table 1.
5. During the 30 min PCR, prepare a 2% agarose gel with GelRed
staining.
6. Remove the PCR plate from the machine directly after finished
program. Withdraw 1 μl of the reaction mixture for analysis
through agarose gel electrophoresis and freeze the plate at
20 C.
7. Analyze the PCR products using 2% agarose gel electrophoresis
to verify the amplification of the template DNA.
3.8.2 Normalization
of PCR Products
A large number of samples (500-600 reactions) can be pooled into
one MiSeq run. The amount of DNA pooled from each PCR
reaction can be normalized through precipitation on magnetic
beads.
1. Thaw the PCR products.
2. Add 20 μl magnetic beads (Mag-Bind total Pure NGS) and
20 μl PCR product to a PCR plate well.
3. Mix by pipetting up and down 10 times.
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Susanne Lüchow et al.
Table 1
PCR program for preparation of amplicons for NGS analysis
PCR program
1
98 C
180 s
2
98 C
10 s
3
68 C
10 s
4
10 s
72 C
Repeat steps 2–4 20
5
72 C
6
4 C
300 s
4. Incubate at room temperature 5 min. The DNA precipitates on
the beads.
5. Place the PCR plate on a magnetic stand and wait for the
solution to become clear. At this point, the magnetic beads
form a small pellet on the side of the well facing the magnet.
6. Carefully aspirate the supernatant from each well.
7. Wash the beads by adding 200 μl 70% ethanol to each well and
incubate for 1 min.
8. Discard the supernatant and leave the plate to air-dry until the
beads are dry. It usually takes ~15 min until the bead pellets
start to crack.
9. Remove the PCR plate from the magnetic stand and elute the
amplicons using 12.5 μl buffer EB.
10. Mix by carefully pipetting up and down 15 times or until the
pellet is fully dissolved.
11. Incubate for 5 min at room temperature.
12. Place the PCR plate on the magnetic stand and wait for
the solution to become clear.
13. Combine 10 μl of each amplicon into a single 1.5 ml tube (see
Note 7).
14. Concentrate the pooled PCR products using a QIAgen PCR
cleanup column according to the manual. Elute in 50 μl TE
buffer.
15. Purify the PCR product by agarose gel electrophoresis and.
Prepare a 2% agarose gel with 60 μl wells and run the gel at
80 mA for 40 min.
16. Excise the band of expected size, taking care not to include
shorter fragments.
Proteomic Peptide-Phage Display of PDZ Domains
59
17. Purify the extracted PCR product using a gel purification kit.
Dissolve the gel completely by an extended incubation in
denaturing buffer QG at room temperature. Do not vortex,
but gently turn the tube up and down. Elute in TE buffer.
18. Quantify the dsDNA concentration using PicoGreen, as
described in Subheading 3.1.
19. Provide the required amount to the sequencing facility following the instructions.
20. Results are typically returned in FASTQ format. The results are
then demultiplexed and matched to the library design as
described elsewhere [16].
4
Notes
1. We quantify the purified PCR product using PicoGreen since
Nanodrop quantification is unreliable for the determination,
and tend to overestimate the concentration.
2. Pick several colonies to make sure that at least one will grow
efficiently.
3. We have inserted a SmaI restriction site in our phagemid vector
in between the annealing sites of the library oligonucleotides,
and ensured that there is no SmaI site in the designed oligonucleotide pool. This step can be omitted.
4. The phage library can be used directly, or reamplified in presence of 0.3 mM IPTG.
5. If there is growth in the other cultures then discard the current
cultures and start a new one).
6. The binding enriched phages can also be analyzed through
clonal phage ELISA and Sanger sequencing, as described
elsewhere [11].
7. Pay attention not to transfer any beads at this stage. In case
there are still some beads in the solution after transfer, then
pellet the magnetic beads using a magnetic stand, and transfer
the solution to a new vial.
Acknowledgments
This work was supported by the European Union’s Horizon 2020
research and innovation program under the Marie SklodowskaCurie grant agreement no. 675341.
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References
1. Songyang Z, Fanning AS, Fu C, Xu J, Marfatia
SM, Chishti AH, Crompton A, Chan AC,
Anderson JM, Cantley LC (1997) Recognition
of unique carboxyl-terminal motifs by distinct
PDZ domains. Science 275:73–77
2. Stricker NL, Christopherson KS, Yi BA, Schatz
PJ, Raab RW, Dawes G, Bassett DE Jr, Bredt
DS, Li M (1997) PDZ domain of neuronal
nitric oxide synthase recognizes novel
C-terminal peptide sequences. Nat Biotechnol
15:336–342
3. Tonikian R, Zhang Y, Sazinsky SL, Currell B,
Yeh JH, Reva B, Held HA, Appleton BA,
Evangelista M, Wu Y, Xin X, Chan AC,
Seshagiri S, Lasky LA, Sander C, Boone C,
Bader GD, Sidhu SS (2008) A specificity map
for the PDZ domain family. PLoS Biol 6:e239
4. Kim J, Kim I, Yang JS, Shin YE, Hwang J,
Park S, Choi YS, Kim S (2012) Rewiring of
PDZ domain-ligand interaction network contributed to eukaryotic evolution. PLoS Genet
8:e1002510
5. Ernst A, Appleton BA, Ivarsson Y, Zhang Y,
Gfeller D, Wiesmann C, Sidhu SS (2014) A
structural portrait of the PDZ domain family.
J Mol Biol 426:3509–3519
6. Zhang Y, Appleton BA, Wiesmann C, Lau T,
Costa M, Hannoush RN, Sidhu SS (2009)
Inhibition of Wnt signaling by Dishevelled
PDZ peptides. Nat Chem Biol 5:217–219
7. Luck K, Trave G (2011) Phage display can
select over-hydrophobic sequences that may
impair prediction of natural domain-peptide
interactions. Bioinformatics 27:899–902
8. Ivarsson Y, Arnold R, McLaughlin M, Nim S,
Joshi R, Ray D, Liu B, Teyra J, Pawson T,
Moffat J, Li SS, Sidhu SS, Kim PM (2014)
Large-scale interaction profiling of PDZ
domains through proteomic peptide-phage
display using human and viral phage peptidomes. Proc Natl Acad Sci U S A
111:2542–2547
9. Garrido-Urbani S, Garg P, Ghossoub R,
Arnold R, Lembo F, Sundell GN, Kim PM,
Lopez M, Zimmermann P, Sidhu SS, Ivarsson
Y (2016) Proteomic peptide phage display
uncovers novel interactions of the PDZ1-2supramodule of syntenin. FEBS Lett 590:3–12
10. Sundell
GN,
Arnold
R,
Ali
M,
Naksukpaiboon P, Orts J, Guntert P, Chi CN,
Ivarsson Y (2018) Proteome-wide analysis of
phospho-regulated PDZ domain interactions.
Mol Syst Biol 14:e8129
11. Rajan S, Sidhu SS (2012) Simplified synthetic
antibody
libraries.
Methods
Enzymol
502:3–23
12. Held HA, Sidhu SS (2004) Comprehensive
mutational analysis of the M13 major coat protein: improved scaffolds for C-terminal phage
display. J Mol Biol 340:587–597
13. Kunkel TA (1985) Rapid and efficient sitespecific mutagenesis without phenotypic selection. Proc Natl Acad Sci U S A 82:488–492
14. Huang H, Sidhu SS (2011) Studying binding
specificities of peptide recognition modules by
high-throughput phage display selections.
Methods Mol Biol 781:87–97
15. McLaughlin ME, Sidhu SS (2013) Engineering and analysis of peptide-recognition domain
specificities by phage display and deep sequencing. Methods Enzymol 523:327–349
16. Ali M, Simonetti L, Ivarsson Y (2020) Screening intrinsically disordered regions for short
linear binding motifs. Methods Mol Biol
2141:529–552
Chapter 4
A Computational Protocol to Analyze PDZ/PBM Affinity Data
Obtained by High-Throughput Holdup Assay
Pau Jané, Lionel Chiron, Goran Bich, Gilles Travé, and Yves Nominé
Abstract
The holdup assay is an automated high-throughput comparative chromatographic retention approach that
allows to measure quantitative binding intensities (BI) for a large number of domain–motif pairs and
deduce equilibrium binding affinity constants. We routinely apply this approach to obtain quantitative
binding specificity profiles of particular PDZ-binding motifs (PBMs) toward the full library of known
human PDZ domains (the PDZome). The quality of the electropherograms extracted from the capillary
electrophoresis instrument at the final step of the holdup assay may vary, influencing the accuracy and
reproducibility of the measurement. By using bioinformatic tools, we can solve these issues to extract more
reliable BIs by means of a better superimposition of the electropherograms. The protocol presented in this
chapter describes the main principles and strategies of our curated method to process holdup data and new
ways to plot and compare the BIs for the PBM–PDZ interactions. For this particular protocol, all the
necessary computing commands are freely available in open Python packages.
Key words Holdup assay, PDZ–PBM interaction, Computational approach, Processing accuracy,
Electropherogram superimposition
1
Introduction
An important subset of protein–protein interactions are mediated
by globular protein domains interacting with short conserved linear
motifs (SLiMs), mostly belonging to intrinsically unfolded regions
of the proteome. Many high-throughput interatomic data allow to
describe protein–SLiM interactions only in a binary way (“binds”
or “does not bind”) [1–4]. The quantification of the binding
affinities would lead to a better understanding of the hierarchies
and specificities of interactions.
In this regard, the holdup assay [5–7] is well suited to extract
quantitative affinity information for an entire network of domain–
SLiM interactions, such as the 266 known human PDZ domains,
and their target motifs, called PDZ-Binding Motifs (PBM). The
holdup assay is a comparative chromatographic retention approach
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021
61
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Pau Jané et al.
based on the reversible binding of constructs to affinity resins
avoiding protein purification and washing steps. In this assay, soluble lysates of recombinant proteins overexpressed in bacteria (e.g.,
PDZ domain) are incubated with avidin beads, previously fully
saturated by either a biotinylated peptide (e.g., PBM) or a reference
(biotin). Once equilibrium is achieved, the resin–liquid mixture is
subjected to a fast filtration. The resulting flow-through contains
the remaining nonbound recombinant protein, which is further
quantified using a capillary electrophoresis instrument. The electropherogram obtained for the PBM–PDZ pair is then superimposed and compared with the one for the reference with biotin–
PDZ. A depletion of the recombinant protein of interest observed
in the flow-through of the resin containing the peptide, as compared to the reference flow-through, indicates that a protein–peptide binding event has occurred. The stronger the depletion of
the recombinant protein of interest, as compared to the reference,
the stronger the protein–peptide binding interaction. Ultimately,
the binding intensity (BI), based on PDZ peak intensities
and related to the interaction strength, is determined, from which
in turn the equilibrium dissociation affinity constant (KD) can be
deduced [8].
A proper estimate of the BI relies on the precise and accurate
pairwise comparison of electropherograms. When dealing with the
same protein extract, the pattern of two electropherograms is
highly conserved in most parts of the graphs, except for the PDZ
peak which may have partly disappeared from the extract by being
specifically retained on the PBM-coated resin. This makes possible
to compare them in an automated way, using computational methods. However, capillary electrophoresis migration may be subject
to variations linked to slight differences of input volume, migration
in the capillary, buffer, well position, or measurement temperature,
possibly altering the peak intensity or the migration of molecules
and therefore their apparent sizes [9, 10]. As a consequence, slight
fluctuations of the peaks on the X- (migration) and/or Y- (sensitivity) axis and baseline perturbation may alter the reproducibility
of the electropherograms and subsequently the accuracy of BI
values (Fig. 1a). For this reason, a strict comparison of two different
electropherograms is not always easy to perform, even when data
are recorded in the same experimental conditions. Therefore, the
use of semiautomated methods to correct and compare the electropherograms, can dramatically improve their superimposition
(Fig. 1b).
Here we present the main lines of a computational protocol to
deal with these issues using commands available in the free Python
Spike package. The accuracy of the results dramatically increases
when one uses this automated approach to improve superimposition of the data. This can be achieved by applying up to five
consecutive processing steps to the electropherograms of both,
the reference and the sample (Fig. 2):
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
63
Fig. 1 A characteristic instance of non-binding, in which the concentration of the PDZ construct of interest
(peak at 59 kDa) should be the same in the flow-through of the peptide-loaded resin and of the biotin loaded
reference resin. The protein at 79 kDa (red peak) is an internal control (here, BSA). (a) Raw data extracted from
capillary electrophoresis. The graphs to be compared are colored green (reference) and black (protein of
interest). Note that, due to sensitivity change between the two electropherograms, a direct superimposition
might lead to a wrong interpretation of the assay. (b) After applying the strategies described in this protocol,
the samples are normalized and can be perfectly superimposed, showing that both peaks at 59 kDa have
exactly the same intensity (i.e., no binding, BI ~0.0)
1. Baseline subtraction in the raw data electropherograms.
2. Normalization of the peak intensities by the use of the added
internal control.
3. X-axis adjustment of the sample electropherogram to the reference one by performing a linear transformation (translation
and dilation).
4. “Secondary” correction of the signal intensities (optional).
5. BI determination deduced from superimposing the electropherograms and calculating the depletion of the peak for the
protein of interest.
The standardization of electropherogram processing leads ultimately to a better comparison and reproducibility between different holdup assays, no matter if performed in the same laboratory or
not. A nice example of the power of applying these strategies can be
found in recent publications, in which we achieved highly accurate
and reproducible results [8, 11].
Although the holdup assay is in principle adaptable to study
diverse binding protein–ligand systems, here we will focus on the
human PDZ domain (the PDZome)–PBM interactions.
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Pau Jané et al.
Caliper data
Change
parameters
Baseline
correction
If error
Normalization
If error
Horizontal
translation and
dilation
Crude extract
in the
sample?
yes
Secondary
correction
no
Superimposition
If error
Check
the data
BI calculation
Fig. 2 Flowchart of the computational strategy presented in this protocol. Circle:
input/output; rectangle with lines: step for processing data; diamond: decision;
inverted trapezoid: manual process done by the user. The “If error” statement
stands for the quality check of the process. When the step output does not match
the quality criteria (See “Checking the data quality” paragraph), it is recommended to return to the baseline correction step in order to change one or
several parameters and to restart the algorithm
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
2
65
Materials
2.1 Capillary
Electrophoresis
Instrument’s Software
Every capillary electrophoresis instrument has its specific software
to retrieve the data.
2.2
Computer
The use of a programming language is necessary to process all the
data. For this project, Python has been chosen.
2.3 SPIKE
Package and Software
Availability
A Python package is available in https://spikedoc.bitbucket.io
[12, 13]. The software can be found in free access here: https://
github.com/lio3867/BiDop
3
Methods
All mentioned steps were performed with in-house Python scripts
using different packages, including SPIKE. This protocol is meant
to be applied to both electropherograms recorded for the reference
and the sample. The protein extract loaded on the reference and the
sample resins should come from the same expression source and in
practice from the same storage tube. This protocol also requires an
internal control added directly to the common storage tube, before
starting the experiment. This control is usually a soluble protein
with an adequate molecular weight, avoiding in particular any
overlap with the molecular weight of the protein of interest. The
peak intensity of the internal control protein (hereafter referred to
as the “internal control peak”) is used for normalization so that the
two electropherograms obtained from the same source tube can be
directly compared with the highest accuracy [6, 7]. Note that it is
possible to include several internal control proteins with distinct
molecular weights in the same sample. This provides the user with a
“plan B,” highly useful when the peak intensity of the regular
internal control is too weak or too intense as compared to the
peak of the protein of interest (hereafter referred to as the “PDZ
peak”), or when the regular internal control peak, but not the rest
of the electropherogram, has been altered due to a migration
defect. If any of the steps fails, the user can try to restart from
point 2 in the “Transforming the input data” section by varying the
default parameters.
3.1 Input Data
Extraction from the
Quantitative Capillary
Electrophoresis
Instrument
1. For each plate analyzed by holdup assay, open the software of
the electrophoresis instrument and make sure that all the ladders and samples are well aligned (see Note 1).
2. Give the proper names to the samples and export the electropherogram’s raw data including molecular weights (see
Note 2).
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Pau Jané et al.
Fig. 3 The first two processing steps of the computational approach. (a) Extraction of the baseline (green). The
peak intensities can be corrected by subtracting the baseline. (b) After subtracting the baseline, the
normalization is performed taking into account one of the internal control peaks (here, the BSA peak around
80 kDa, in red)
3.2 Transforming
the Input Data
1. Among other information, the extracted raw data files contain
three columns with migration time, converted molecular
weights, and fluorescence signal. Adjust their format to make
them easy to read in order to manage the data for the following
steps. A full access to the data allows to plot them. In particular,
it is possible to replot the results of each individual in order to
give a better understanding of the process and to detect any
problem that would occur during the procedure.
2. Perform the baseline correction for both the reference and the
sample electropherograms using the BC module of the SPIKE
package [12, 13] (Fig. 3a). This will adjust the real intensity of
each electropherogram by subtracting the background signal.
The user may need to adjust the parameters, for instance if the
overexpressed domain migrates at a different size or with distinct expression level than the MBP-PDZ domain fusion used
in our own assay.
3. Perform the normalization by selecting the internal control
peak for both the reference and the sample (Fig. 3b). This
allows to correct for potential variations of overall protein
concentration between the two electropherograms, due to
slight sensitivity changes or volume variations that may occur
after the binding step, in particular when reagents are added to
the samples prior to the caliper measurements.
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
67
Fig. 4 Examples of different binding cases, visualized after successful data treatment. The peaks at 17 and
59 kDa correspond to the internal control protein (here, lysozyme), and the PDZ construct (protein of interest),
respectively. Red: normalization peak. Green: peak of the PDZ construct as seen in the sample corresponding
to the flow-through of the biotinylated PBM-loaded avidin resin. (a) Both electropherograms are perfectly
aligned, and both PDZ peaks are undistinguishable: this PDZ construct did not detectably bind to the PBM. (b)
Both electropherograms are perfectly aligned, yet the PDZ peak intensity detected in the flow-through of the
PBM-loaded resin has considerably decreased as compared to the PDZ peak of the flow-through of the biotinloaded reference resin: this PDZ construct strongly bound to the PBM. Black: PDZ peak as detected in the
reference, corresponding to the flow-through of the biotin-loaded avidin resin
4. Adjust the X-axis of the sample electropherogram to better
match the reference electropherogram using a linear transformation (shift and dilation/contraction). For this purpose, the
difference of signal between the two electropherograms is
minimized using a least square approach by focusing on a
molecular weight window that contains the peak of the protein
of interest.
5. Superimpose the two electropherograms. When everything
works properly, two different scenarios may occur: (a) both
electropherograms are strictly identical, so that no binding is
observed (BI ~0.00; See below) (Fig. 4a); (b) only the peak
corresponding to the molecular weight of the overexpressed
protein has significantly decreased (Fig. 4b) (see Note 3),
indicative of an interaction between the PDZ and the PBM.
Noteworthy enough, the first scenario (no detectable binding)
is the most likely to occur (in our experience, 80–90% of the
cases).
6. No matter if the superimposition worked properly or not, the
following part of the protocol details how to check the accuracy
of your results.
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3.3 Checking
the Data Quality
In order to assay the overall quality of the electropherograms, we
determined empirically several quality criteria that can be fulfilled:
1. Quality of the normalization peak: The intensity of the normalization peak should be in the same order of magnitude as for
the PDZ peak observed in the reference.
2. Quality of the detection signal: Both normalization and PDZ
peaks should be in the linear range of the capillary electrophoresis instrument.
3. Quality of the PDZ expression: The ratio of the PDZ intensity
by the average of signal within the crude extract range (typically
20–50 kDa) should be as high as possible, and never below 1.5.
4. Quality of the PDZ construct dilution: We experimentally
observed a threshold intensity of the PDZ peak (~200 units
of Fluorescence) below which the processing might be critical.
5. Quality of the PDZ construct: The difference between experimental (from Caliper data after conversion) and theoretical
(according to the sequence of the construct) molecular weights
should be as low as possible, and not exceed 5 kDa (see
Note 4).
6. Quality of the electropherogram superimposition on X-axis:
The linear transformation used to match the X-axis of the two
electropherograms should be as neutral as possible (i.e., dilation coefficient and shift as close as possible to 1.0 and 0.0,
respectively).
7. Quality of the electropherogram comparison on Y-axis: It is
also possible to improve the accuracy of the signal comparison
when the overexpressed sample is not purified and contains
peaks from the bacterial crude extract (see Note 5). If used,
this optional processing step should lead to a correction factor
as close to 1.00 as possible.
3.4 Extracting BIs
and Other Data
1. Extract the BI of the peak of the protein of interest. After the
superimposition, the Binding Intensity (BI) is calculated by
subtracting the peak intensity of the sample (Ilig) from the
PDZ peak intensity of the reference (Iref), and then dividing
by Iref (Eq. 1). With this expression, 1.00 is the maximal value
that can be obtained for BI. As previously shown [6], BI >0.20
represents a high-confidence binding event. Below this threshold, 0.10 < BI <0.20 may still represent a significant binding
event, depending on data quality. For BI <0.10, we consider
that no binding has been detected.
BI ¼
I ref I lig
I ref
ð1Þ
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
69
2. The electropherograms contain a lot of information besides the
visual part. In addition to the BI, it is worth to save additional
values such as peak intensities and positions, correction factors,
and baseline levels (see Note 6). This might help for further
analysis and can provide useful answers to several questions: for
instance, is the overexpressed domain stable and expressed
always at the same molecular weight?
3.5 Storing
and Plotting the Data
1. To combine all the data obtained with different plates, export
them into tables and create a repository data base, for instance
by using the SQLite3 Python engine. This type of interface
allows to “ask queries” and retrieves all the needed data from
this database at any moment for further comparisons and plots.
2. One way to plot the data from the database is to extract a
Binding Profile (Fig. 5a). The Binding Profile for a given
PBM is a bar plot displaying Binding Intensity (BI) values in
which all the PDZ domains are ranked from the strongest to
the weakest binder. This plotting mode captures the specificity
of the recognition, imbedded in the curvature of the profile.
3. Another way to plot the data is to create a circle for each PDZ–
PBM pair tested, whose diameter is proportional to the BI
value, and to stack all the generated circles in the lower part
of the largest one. The binding strengths, as well as the specificity, can then be easily appreciated by comparing the differences of the diameters (Fig. 5b).
4. The same type of plots can be generated by centering all the
circles at the origin (Fig. 5c).
Fig. 5 Different visualization modes as illustrated with RSK1 holdup data. (a) The wild-type RSK1 BI profile.
The strongest PDZ binders are ranked from left to right of the plot in decreasing order along the X-axis. The
curvature of the profile shows the specificity of the PBM–PDZ binding. Threshold for the confidence value of
binding is set at 0.20 (yellow dotted line). (b) Circular plot, the stronger the color, the higher the BI. This makes
it easier to stress out the PBM specificity. (c) Is shown an alternative circular plot representation in which all
the circles are centered at the origin
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Pau Jané et al.
Fig. 6 Comparison of different PDZome binding profiles as illustrated with holdup data obtained for two RSK1
PBMs (the wild-type RSK1 and the RSK1 phosphorylated at p-3 position—called RSK1p-3). (a) Binding profiles
for the indicated peptides. PDZ domains in top and middle panels are ranked on the basis of BI values obtained
for RSK1. In the bottom panel, PDZ domains are ranked on the basis of their BI values obtained for RSK1p-3. (b)
Heatmap profiles for the indicated peptides. PDZ domains in top and middle panels are ranked on the basis of
values of -log(KD) obtained for RSK1. In the bottom panel, PDZ domains are ranked on the basis of values of
log(KD) obtained for RSK1p-3. (c) Comparison of the two different assayed PBMs. The higher the BI, the
stronger the color. This makes it easier to stress out the difference in affinity and specificity for the two PBMs
5. The usual approach to compare two BI profiles consists of
plotting the PDZome binding specificity profiles of the two
PBMs, both ranked from highest to smallest BI value, and
adding in between the two profiles an extra plot showing the
BI values of the second PBM sorted according to the ranking of
the first PBM [6, 8, 11] (Fig. 6a).
6. Alternatively, the BI values can also be transformed into -log
(KD), using the following equation (Eq. 2) [8] (see Note 7):
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
ð½PDZtot BI ½PDZtot Þ ð½PBMtot BI ½PDZtot Þ
logðK D Þ ¼ log
BI ½PDZtot 71
ð2Þ
For a given temperature, these values are proportional to
ΔG, allowing to rank all the affinity values in a continuous
heatmap (like in the binding profiles), and making it easier for
experimentalists to compare the affinities using scaled values
widely known among all. The same kind of rearrangement as in
Fig. 6a can be done with the colored heatmaps according to
enthalpy changes (Fig. 6b).
7. Lastly, the circle plots shown in Fig. 5c can be divided into as
many sectors as samples, allowing to compare several samples at
one glance (Fig. 6c).
4
Notes
1. Electropherograms presented in this chapter are obtained using
the Caliper GXII LabChip system (PerkinElmer). In some cases
the operating system of the capillary instrument fails to detect
the ladders at the proper molecular weights. This issue leads to
an inconsistent X-axis, making very difficult to superimpose
two electropherograms because of peak misalignment (Fig. 7).
The problem is often linked to the appearance of some
not-expected extra peaks (e.g., contaminants or detection artifacts). To solve this problem, on the LabChip software, exclude
the additional peaks and if needed, include the proper ones
according to the molecular weights of the used ladders. A
similar process may have to be applied to the sample peaks.
Such a step often helps to optimize the data superimposition.
2. Exporting the electropherograms. In the Caliper GXII LabChip software, go to “Tools” and select “Sample Name Editor” to rename the samples. Select the data to be exported and
click “Export” in the file tab menu. A window will pop up: click
on “Raw Data” and “Include Size Data” to export all the
necessary points of the electropherograms.
3. We consider an intensity decrease as significant when the amplitude change is much larger (at least 3 to 5 times more) than the
variability observed in zones presenting constant signal (e.g.,
around 100 kDa).
4. We experienced once a global shift of the molecular weights for
all the ladders and samples. As far as it affects all the samples
equally, this molecular weight shift will not affect the BI calculation while using this protocol.
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Pau Jané et al.
Fig. 7 Example of misalignment as seen on both the electropherograms (top) and the artificial gels (bottom).
The misalignment of the ladder 3 is particularly well visible when displaying all the ladders. This shift in
positioning generates a wrong molecular weight scale for all the samples calibrated with this ladder. This
happens due to contaminants that are misleadingly recognized as a lower marker or wrong ladder peaks. LM
(green): lower marker; Xsys (red): system peak; L (black): ladder peaks
5. The crude extract peaks from the bacterial expression can also
help to perform a secondary correction in addition to the
internal control peaks, since the intensities of those bacterial
protein peaks should be the same in the two electropherograms
to be compared. If one wishes to use the crude extract peaks as
a secondary correction option, a range excluding the normalization peak and the peak of interest should be defined. We have
noticed that the Y-axis superimposition can often be further
improved by applying this secondary correction (Fig. 8).
6. We keep track of binding strengths, intensities and positions of
the internal controls, intensities and positions of the PDZ
domains, range of the crude extract and its intensity, shift/
dilation coefficients in the X-axis, and correction factors when
applying the secondary correction. We store all these data in
CSV files and combine them in a unique Data Base using
SQLite3. We then retrieve the data by asking “specific queries”
and manage the large amount of data with the Pandas package
provided by Python.
7. All the concentrations in Eq. 2 are known except that of the
PBM. For this purpose, affinities of several PDZ domains for a
A Computational Protocol to Analyze PDZ/PBM Affinity Data Obtained. . .
73
Fig. 8 Improving data quality by use of secondary correction. (a) Instance of a superimposition done by using
only the internal control (lysozyme, in red) for normalization. Note that several peaks in the chromatograms
(PBM peptide flow-through in hatched blue line, biotin reference flow-through in continuous black line),
remain misaligned. The PDZ construct peak (55 kDa) seems less intense in the PBM flow-through than in the
biotin reference flow-through, which would indicate a binding event. (b) Same instance, further treated using
the secondary correction procedure that takes into account all peaks of crude extract proteins in the
20–50 kDa range. This procedure allows for a better adjustment of most of the peaks in the crude extract
(examples are pointed out by circles). The intensities of the PDZ construct peak in the reference and sample
electropherograms are now very similar, indicating a no-binding event (BI ~0.00). Note that, in a PDZomebinding assay, no-binding events are by far the most likely events, since in our experience only 10–20% of the
PDZ domains are found to detectably bind to a given PBM. This must be kept in mind when analyzing the data:
provided that the caliper runs are correct, a full superimposition of all peaks in both chromatograms, including
the PDZ construct peak, is the most likely situation
given PBM are measured by fluorescence polarization assay and
subsequently used to estimate the concentration of that
PBM [8].
Acknowledgments
This work received institutional support from Centre National de la
Recherche Scientifique (CNRS), Université de Strasbourg, Institut
National de la Santé et de la Recherche Médicale (INSERM) and
Région Alsace. The work was supported by funding from the
European Union’s Horizon 2020 research and innovation program
under the Marie Sklodowska-Curie grant agreement No 675341,
by the Ligue contre le Cancer (équipe labellisée 2015), by the
National Institutes of Health (Grant R01CA134737), and by the
French Infrastructure for Integrated Structural Biology (FRISBI).
74
Pau Jané et al.
References
1. Tonikian R, Zhang Y, Sazinsky SL, Currell B,
Yeh JH, Reva B et al (2008) A specificity map
for the PDZ domain family. PLoS Biol 6:e239
2. Luck K, Travé G (2011) Phage display can
select over-hydrophobic sequences that may
impair prediction of natural domain–peptide
interactions. Bioinformatics 27:899–902
3. Smith CA, Kortemme T (2010) Structurebased prediction of the peptide sequence
space recognized by natural and synthetic
PDZ domains. J Mol Biol 402:460–474
4. Jadwin JA, Ogiue-Ikeda M, Machida K (2012)
The application of modular protein domains in
proteomics. FEBS Lett 586:2586–2596
5. Charbonnier S, Zanier K, Masson M, Travé G
(2006) Capturing protein-protein complexes
at equilibrium: the holdup comparative chromatographic retention assay. Protein Expr Purif
50:89–101
6. Vincentelli R, Luck K, Poirson J, Polanowska J,
Abdat J, Blémont M et al (2015) Quantifying
domain-ligand affinities and specificities by
high-throughput holdup assay. Nat Methods
12:787–793
7. Duhoo Y, Girault V, Turchetto J, Ramond L,
Durbesson F et al (2019) High-Throughput
production of a new library of human single
and tandem PDZ domains allows quantitative
PDZ-Peptide interaction screening through
high-throughput holdup assay. Methods Mol
Biol 2025:439–476
8. Gógl G, Jané P, Caillet-Saguy C, Kostmann C,
Bich G, Cousido-Siah A, Nyitray L,
Vincentelli R, Wolff N, Nominé Y,
Sluchanko N, Travé G (2020) Dual specificity
PDZ- and 14-3-3-Binding motifs: a structural
and interactomics study. Structure 28
(7):747–759.e3
9. Altria KD, Fabre H (1995) Approaches to optimisation of precision in capillary electrophoresis. Chromatographia 40:313–320
10. Ross GA (1997) Precision and quantitation in
capillary electrophoresis. In: Shintani H,
Polonský J (eds) Handbook of capillary electrophoresis applications. Springer, Dordrecht,
pp 41–55
11. Gógl G, Biri-Kovács B, Durbesson F, Jané P,
Nominé Y et al (2019) Rewiring of RSK–PDZ
interactome by linear motif phosphorylation. J
Mol Biol 431:1234–1249
12. Tramesel D, Catherinot V, Delsuc MA (2007)
Modeling of NMR processing, toward efficient
unattended processing of NMR experiments. J
Magn Reson 188:56–67
13. van Agthoven MA, Chiron L, Coutouly MA,
Delsuc
MA,
Rolando
C
(2012)
Two-dimensional ECD FT-ICR mass spectrometry of peptides and glycopeptides. Anal
Chem 84:5589–5595
Chapter 5
Study of PDZ–Peptide and PDZ–Lipid Interactions
by Surface Plasmon Resonance/BIAcore
Pascale Zimmermann and Antonio Luis Egea-Jimenez
Abstract
Surface plasmon resonance (SPR)/BIAcore technology enables the characterization of molecular interactions, including determination of affinities and kinetics. In BIAcore, one of the interaction partners (the
ligand) is immobilized on a chip and the other (the analyte) is provided in solution. BIAcore allows to study
association and dissociation rates in real time without the use of labeling. BIAcore can be applied to
molecular interactions involving small compounds and biological macromolecules such as proteins, lipids,
nucleic acids, or carbohydrates. Here we describe protocols for the measurements of PDZ domain–peptide
(oriented biotinylated peptides), PDZ domain–liposomes (lipid membranes), and PDZ–lipid–peptide
tripartite interactions.
Key words PDZ, Lipids, Lipid interaction, Liposomes, Peptide interaction, BIAcore, L1 sensor chip,
SA sensor chip
1
Introduction
PDZ domains are globular domains composed of approximately
90 amino acids. They share a common fold comprising six β-strands
arranged in a β-sandwich and two α-helices capping each end
[1]. There are approximately 270 PDZ domains in the human
proteome, distributed in more than 150 PDZ proteins [2]. PDZ
domains are the most common interacting modules in humans,
recognizing short peptide motifs, called PDZ binding motifs, most
often situated at the cytosolic tail of target proteins such as membrane receptors, adhesion molecules, or ion channels, among
others [3]. Involved in the formation and function of signal transduction complexes, PDZ domain–peptide interactions are key regulators in many cellular pathways [4]. Some PDZ domains can
interact with lipids and even act as dual specificity modules displaying both peptide and membrane lipid binding activities. Lipid
partners of PDZ domains include phospholipids such as phosphoinositides [5–10] and phosphatidylserine [11] or the neutral lipid
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021
75
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Pascale Zimmermann and Antonio Luis Egea-Jimenez
cholesterol [12]. Lipid interactions are important for the organization, segregation and trafficking of PDZ protein complexes to
specific cell compartments. For example, crystal structures and
complementary biophysical, biochemical, and cell biological
approaches demonstrate that tripartite PDZ domain–peptide–
phosphatidylinositol 4,5-bisphosphate (PIP2) interactions function
in membrane compartmentalization and dynamics to control noncanonical Wnt signaling [6]. To better understand the biology of
PDZ proteins, it is thus important to clarify the crosstalk between
PDZ domains, cognate peptides, and lipids. Here we describe
protocols for such interaction studies using BIAcore. We explain
how to measure the binding of a PDZ domain/protein to a biotinylated peptide (Figs. 1, 2, and 3), specific lipids embedded in
liposomes (Figs. 4 and 5) as well as tripartite interactions (Fig. 6).
Further information and the description of complementary biochemical and cell biological approaches can be found in manuscripts from Zimmermann et al.
2
Materials
2.1 Lyophilized
Synthetic Biotinylated
Peptides
1. Peptide. The use of N-terminally biotinylated peptides, ideally
15 mer long or longer, purity ~95% is recommended.
2. Storage of lyophilized peptides. Peptides should be stored at
80 C. Most peptides stored at this temperature will remain
stable for several years.
3. Solubility. Try to dissolve a small amount of peptide in deionized water. If the peptide is insoluble, sonication may help.
The solubility of basic peptides can be increased by the addition
of 10% (v/v) acetic acid. In case of acidic peptides it is recommended to add 1% NH4OH. For hydrophobic peptides it
might be necessary to add organic solvents such as DMSO or
Acetonitrile (from 5 to 50%). Stock solutions at 1 mg/ml are
usually stable for several months when conserved at 80 C.
2.2 Liposomes: We
Recommend to Use
Liposomes Mimicking
the Lipid Composition
of the Biological
Membranes of Interest
2.3
BIAcore
1. Lipid solvent. Chloroform–methanol solution (1:1). Store at
room temperature in a bottle wrapped with aluminum foil and
sealed with Teflon caps (see Note 1).
2. Preparation of lipid stocks (from powder). Use chloroform–
methanol (1:1) to solubilize and aliquot into glass vials sealed
with Teflon caps. The stock of lipids can be stored at 20 C
for months.
All buffers and solutions for BIAcore should be filtered through a
0.22 μm filter. Degassing before use is also essential.
PDZ-Peptide and PDZ-Lipid Interactions by SPR
77
Fig. 1 Schematic representation of SA sensor chip capturing biotinylated ligands.
Streptavidin is covalently attached to dextran matrix for the nonreversible
capture of biotinylated molecules
Fig. 2 Representation of a sensorgram: a real-time plot of binding response over time. Upon injection of the
analyte (sample) (b), the signal increases till equilibrium (c) is reached, allowing observation of the association
rate. When the analyte is no longer provided (d), dissociation can be observed. After regeneration (e), another
measurement can take place
1. BIAcore Running buffer: 25 mM HEPES pH 7.4 and 150 mM
NaCl. Weigh 5.957 g of HEPES and 8.766 g of NaCl and
transfer to a glass beaker. Add about 900 mL of deionized
water to the glass beaker. Mix and adjust the pH with NaOH
to 7.4. Transfer to a 1 L graduated cylinder and adjust to 1 L
78
Pascale Zimmermann and Antonio Luis Egea-Jimenez
Fig. 3 Sensorgrams corresponding to five analyte injections (left). Maximum signals obtained at equilibrium
(Req-max) (crosses) are plotted as a function of protein concentration for the determination of an apparent
dissociation constant KD (right). Apparent dissociation constant KD value corresponds to Req-max/2 (arrow)
Fig. 4 Schematic representation of an L1 sensor chip capturing liposomes. The L1 chip is composed of a
dextran matrix substituted with lipophilic residues for the capture of lipids/liposomes
Fig. 5 (a) Representation of single-cycle kinetics. Each arrow corresponds to the start of sample injection.
Note that the samples are injected sequentially in the same cycle. (b) The start of each sample injection is
adjusted to time 0 to obtain an MCK-like graph. (c) Req-max is plotted as a function of protein concentration.
Apparent dissociation constant KD value corresponds to Req-max/2 and is represented with an arrow
PDZ-Peptide and PDZ-Lipid Interactions by SPR
79
Fig. 6 A fixed concentration of analyte is injected with increasing concentrations
of peptide (a) or lipid (b) over immobilized peptide on a BIAcore SA sensor chip.
Note a competition with the peptide in solution (a) and a positive cooperative
effect with the lipid (b) over the immobilized peptide
with deionized water. Store at 4 C. Add 5 μL of Tween 20 to
obtain a final concentration of 0.005% if the running buffer is
going to be used on a SA sensor chip with biotinylated ligands
(see Note 2).
2. BIAcore extra wash solution: 50% isopropanol, 1 M NaCl, and
50 mM NaOH. Weigh 0.4 g of NaOH and 11.688 g of NaCl
and transfer to a glass beaker. Add about 100 mL of deionized
water to the glass beaker. Mix and transfer to a 100 mL
graduated cylinder and adjust to 100 mL with deionized
water to obtain a final concentration of 2 M NaCl and
100 mM NaOH. Mix this solution with an equal volume of
isopropanol.
3. BIAcore regeneration solution: 1 M NaCl and 50 mM NaOH.
Weigh 0.4 g of NaOH and 11.688 g of NaCl and transfer to a
glass beaker. Add about 200 mL of deionized water to the glass
beaker. Mix and transfer to a 200 mL graduated cylinder and
adjust to 200 mL with deionized water.
4. BIAcore detergent solution A: 1% β-octylglucoside. Weight
0.5 g of β-octylglucoside and transfer to a glass beaker. Add
50 mL of deionized water to the glass beaker. Mix and transfer
to a 50 mL graduated cylinder and adjust to 50 mL with
deionized water.
5. BIAcore detergent solution B: 0.5% sodium lauryl sulfate
(SDS). Weight 0.25 g of sodium lauryl sulfate and transfer to
a glass beaker. Add 50 mL of deionized water to the glass
beaker. Mix and transfer to a 50 mL graduated cylinder and
adjust to 50 mL with deionized water.
80
Pascale Zimmermann and Antonio Luis Egea-Jimenez
6. BIAcore liposomes wash solution: 100 mM NaOH. Weight of
0.4 g of NaOH and transfer to a glass beaker. Add about
100 mL of deionized water to the glass beaker. Mix and transfer to a 100 mL graduated cylinder and adjust to 100 mL with
deionized water.
2.4
Chips
1. Sensor chip SA. The streptavidin (SA) sensor chip is used to
immobilize biotinylated molecules.
2. Sensor chip L1. This sensor chip L1 is used to immobilize
reconstituted liposomes.
3
Methods
Carry out all procedures at room temperature unless otherwise
specified.
3.1 Immobilizing
Biotinylated
Compounds
on BIAcore SA
Sensor Chip
For a BIAcore T200 equipment. The buffer solution is filtered
through a 0.22 μm filter and the instrument is cleaned with Desorb
command before docking the sensor chip (see Note 3). Set the
temperature to 25 C.
1. The SA sensor chip is designed to capture biotinylated peptides, immobilizing and using these as ligands (it can also be
used to capture biotinylated lipids although data are less reliable from a biological point of view than with liposomes that
mimic membrane compartments) (see Note 4). SA sensor chip
provides a sensor surface with covalently attached streptavidin.
Since the streptavidin–biotin interaction is very strong, the
capture is essentially irreversible (Fig. 1).
2. SA sensor chip is acclimated to room temperature before being
inserted into the BIAcore instrument (see Note 5).
3. Insert the SA sensor chip and prime with the BIAcore Running
Buffer.
4. The surface of the chip is conditioned with three consecutive
injections of 5 μL of BIAcore Running Buffer at a flow rate of
10 μL/min.
5. Start a new cycle at a flow rate of 10 μL/min and select only the
flow cell that will be used for immobilization of the biotinylated
peptide, previously solubilized in deionized water as described
in Subheading 2.1, (see Note 6).
6. The immobilization level will determine the binding capacity of
the surface and the maximum response (Rmax). In general, low
Rmax is required for kinetic analysis whereas a high Rmax is
preferable in concentration measurements. The Rmax value
can be calculated using the following formula:
PDZ-Peptide and PDZ-Lipid Interactions by SPR
Rmax ¼
81
Analyte Molecular Weight
immobilized amount
Ligand Molecular Weight
stoichiometric ratio
7. The last step of the immobilization procedure consists in running 5 μL of BIAcore extra wash solution at a flow rate of
10 μL/min (see Note 7).
8. Note the total resonance units (RUs). RU provide information
about the level of the ligand immobilized on the surface of the
chip (see BIAcore manual for more information). The SA
sensor chip is ready to be used.
9. If appropriate, switch to another channel and proceed with
another immobilization of biotinylated compound on the chip.
10. For considerations about reference channel and blank subtraction, see Note 8.
11. It is recommended to include a negative control (see Note 9),
and at least one replicate to evaluate the reproducibility and
robustness of the assay.
3.2 kon/koff
Determination Using
a Biotinylated
Peptide-Loaded
BIAcore SA
Sensor Chip
The multicycle kinetics (MCK) can be used for the study of PDZ
domain–peptide interactions. In the MCK approach, analyte injections are done in separate cycles and a regeneration step is performed at the end of each cycle.
1. Start a new cycle at a flow rate of 30 μL/min in BIAcore
running buffer and verify the optimal equilibration of the
sensor surface corresponding to a flat baseline (Fig. 2a).
2. Inject the analyte at a flow rate of 30 μL/min in BIAcore
running buffer. It is recommended to test a range of concentrations, between 0.1 and 10 times the known/expected KD
(see Note 10).
3. The association time (Fig. 2b) will be long enough to reach the
steady state (Fig. 2c) and will depend on the kinetics of the
interaction between the ligand and the analyte. The steady state
indicates that the number of association events is equal to the
number of dissociation events. An injection time between
90 and 120 s is usually appropriate to observe association
between PDZ domain and cognate peptides (see Note 11).
The association rate constant (kon) will be obtained from this
part of the curve.
4. The dissociation (Fig. 2d) is observed upon injection of
analyte-free BIAcore running buffer, usually at a flow rate of
30 μL/min in BIAcore running buffer. High affinity interactions usually have slow dissociations. A dissociation time of
120 s is recommended for the measurement of PDZ domain–
peptide interactions. The dissociation rate constant (koff) will
be obtained from this part of the curve.
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Pascale Zimmermann and Antonio Luis Egea-Jimenez
5. The surface is regenerated through 1 pulse of 30 μL of BIAcore
regeneration solution at a flow rate of 30 μL/min (Fig. 2e).
This step will remove the analyte from the surface whereas the
ligand will stay intact. In order to reuse the chip, it is important
to obtain a baseline similar to the one observed at the beginning of the experiment (Fig. 2a, f) (see Note 12).
3.3 Calculate Steady
State Affinity
in Multicycle Kinetics
Analysis
The analysis of steady-state binding data can be applied to 1:1
binding modes (such as in the case of most PDZ domain–peptide
interactions) and it can be performed with the BIAevaluation software provided with the instrument (command “Kinetics/Affinity”)
(Fig. 3).
3.4 Preparation
of Large Unilamellar
Vesicles (LUVs) by
Extrusion
1. Mix the lipids from the stock solutions at the relevant molar
ratio.
2. Dry the lipid mixture under a stream of nitrogen to avoid lipid
oxidation. The lipid films will be kept under high vacuum for at
least 2 h in order to discard residual organic solvent. If the
dried lipids are not used immediately, seal the tube with parafilm and store it at 20 C for not more than 1 week.
3. Hydrate the lipid films by addition of BIAcore Running Buffer
to generate a suspension containing 1.5 mM of total phospholipids. Vortex thoroughly to emulsify the lipid mixture, and use
also a bath sonicator to help lipid suspension.
4. Subject the lipid suspension to 8 rounds of freezing (liquid N2)
and thawing (bath set at 40 C). This step will increase the
efficiency of entrapment of water-soluble compounds.
5. Load the sample into one of the gastight syringes and place the
syringe at one end of the extruder (see Note 13). The liposome
preparation should be extruded through 0.1 μm polycarbonate
membranes (see Note 14). A minimum of eleven passes
through the membrane is recommended for most lipids (see
Note 15).
6. You can store the solution containing the liposomes/large
unilamellar vesicles (LUVs) at 4 C. The stock of LUVs is
usually stable for no more than 3–4 days at 4 C.
7. Do not forget to clean the extruder with BIAcore detergent
solution A after use, then rinse it thoroughly with deionized
water, and dry it before storage.
3.5 Immobilizing
LUVs on BIAcore L1
Sensor Chips
For a BIAcore T200 equipment. The buffer solution is filtered
through a 0.22 μm filter and the instrument is cleaned with Desorb
command before docking the sensor chip. Set the temperature to
25 C.
PDZ-Peptide and PDZ-Lipid Interactions by SPR
83
1. The lipid capturing surface L1 sensor chip is used for the
immobilization of the vesicles (Fig. 4) prepared as explained
under Subheading 3.4. The L1 sensor chip is equilibrated to
room temperature for 15–30 min before being inserted into
the BIAcore instrument.
2. Insert the L1 sensor chip and prime the instrument with the
BIAcore Running Buffer (25 mM HEPES, pH 7.4,
150 mM NaCl).
3. Start by washing the surfaces that will be used for the vesicle
immobilization with 20 μL of BIAcore detergent solution A at
a flow rate of 5 μL/min, followed by 20 μL of BIAcore detergent solution B at a flow rate of 5 μL/min.
4. Start a new cycle running at a flow rate of 10 μL/min and select
only the flow cell that will be used for immobilization of lipids.
The surface will be conditioned with a 10 μL injection of
BIAcore detergent solution A.
5. Switch the flow rate to 5 μL/min and inject the preparation of
vesicles (Fig. 4). The amount of liposomes attached on the L1
chip will depend on the concentration of liposomes injected
over the chip and the charge of the liposomes (see Note 16).
6. Inject the LUVs till a response of 4000 resonance units (RUs)
is reached.
7. Wash the surface with 20 μL of BIAcore liposomes wash solution at a flow rate of 100 μL/min to remove loosely bound
liposomes.
8. Note the total RUs immobilized; the surface is ready to
be used!
9. If necessary, switch to another channel and proceed with
another immobilization.
3.6 Start
an Experiment
in BIAcore
with an L1 Chip
Single-cycle kinetics (SCK) is recommended for the study of PDZ
domain–liposome interactions. This approach is always valuable
when the surface is difficult to regenerate accurately. The analyte
is injected sequentially in a single cycle, with no regeneration step
between injections (see Note 17) (Fig. 5a).
A predefined method with BIAcore T200 is available “command Single cycle kinetics” (5 sample concentrations per cycle). It is
not possible to modify the predefined variables including the number of analyte injections. Yet other parameters such as flow rate or
association time can be adapted to the experiment.
The following settings are recommended for the measurement
of PDZ domain–lipid interactions.
1. The analyte will be injected at a flow rate of 30 μL/min in
BIAcore running buffer.
84
Pascale Zimmermann and Antonio Luis Egea-Jimenez
2. The contact time of the analyte with the surface will be around
120 s.
3. At the end of the experiment, wash the cells with 10 μL of
BIAcore detergent solution A at a flow rate of 10 μL/min and
start a new immobilization of LUVs as described under Subheading 3.5 (see Note 18).
4. To determine an apparent KD value, the same principle as
described in Subheading 3.3 can be applied. The response for
each injection will be extracted and fitted considering the injection at time 0 (Fig. 5b). The maximal signals obtained at
equilibrium (Req-max) are plotted against protein concentrations (Fig. 5c).
5. The data analysis can be performed using the BIAevaluation
software provided with the BIAcore T200 instrument. The
affinity constants (association and dissociation constants) can
be calculated from the kinetic measurements.
6. For the study of tripartite complexes on PDZ domain–lipid
membrane interactions see Note 19.
3.7 Study
of Tripartite
Complexes in BIAcore
with a SA Sensor Chip
MCK can be used for the study of the formation of a tripartite
complex when the ligand is immobilized on the chip.
1. Immobilize the biotinylated peptide or lipid of interest as
indicated under Subheading 3.1.
2. Start a new cycle at a flow rate of 30 μL/min in BIAcore
running buffer and verify the optimal equilibration of the
sensor surface (flat baseline).
3. Incubate and inject a fixed concentration of analyte (PDZ
protein) together with the soluble peptide or lipid at a flow
rate of 30 μL/min in BIAcore running buffer.
4. The association and dissociation times together with the regeneration step are the same as described under Subheading 3.2.
5. The concentration of the soluble peptide or lipid is increased in
each injection/cycle whereas the analyte remains constant.
6. Equilibrium responses are plotted against the logarithm of the
peptide (Fig. 6a) or lipid (Fig. 6b) concentration and the data
fitted to the equation log (agonist) versus response.
The Hill coefficient (nH) is calculated by adjusting the
curve to the Hill–Langmuir equation.
nH < 1 Negative cooperative binding. The PDZ protein is
bound to the immobilized peptide, the presence of another
ligand in solution competes with this PDZ domain–peptide interaction (Fig. 6a).
PDZ-Peptide and PDZ-Lipid Interactions by SPR
85
nH > 1 Positive cooperative binding. The PDZ protein is
bound to the immobilized peptide, its affinity for the
other ligand (peptide or lipid in solution) increases
(Fig. 6b).
nH ¼ 1 Noncooperative binding. The affinity between the
PDZ protein and the immobilized peptide is not affected
by a presence of the compound in solution.
4
Notes
1. Work with organic solvents in a chemical fume hood. The work
solution has to be stored in tightly closed containers in cool dry
well ventilated areas.
2. The addition of 0.005% Tween 20 will prevent nonspecific
binding. Yet DO NOT add Tween 20 for running an experiment with vesicles on an L1 sensor chip.
3. Desorb step is particularly important after experiments using
other biotinylated molecules in order to avoid crosscontamination.
4. We will refer to biotinylated peptides in this method paper.
5. Sensor chips are stored at 4 C. The sensor chip will be acclimated to room temperature just before the experiment in order
to prevent condensation on the chip surface.
6. Make a serial dilution 103, 104, 105, and 106, and start
using the most diluted solution for the immobilization. The
interaction between biotin and streptavidin is stable and virtually nonreversible (kD ~ 1030). Therefore short injections at
low concentration help to reach the number of immobilized
RUs wanted and not more.
7. This wash solution does not have to pass over the sensor surface
and only the needle and the sample loop should be washed.
8. The bulk refractive index difference has to be corrected by
using a cell as a reference channel (preferably flow-cell 1). The
signals from the reference channel corresponding to the bulk
shift and nonspecific binding will be subtracted. It is also
recommended to measure two “zero injections” with running
buffer, preferably before and after the samples, to perform a
blank subtraction. The blank and reference subtraction are
referred to as “double reference subtraction.”
9. It is important to prepare different analyte samples in the same
running buffer in order to avoid optical artefacts (bulk shift).
10. For the study of PDZ domain–peptide interaction, we recommend to add a positive control peptide when available and a
negative control corresponding to a peptide where the last four
86
Pascale Zimmermann and Antonio Luis Egea-Jimenez
amino acids (PDZ-binding motif) are scrambled or the 2 or
3 last amino acids are omitted. We also recommend peptides to
be not shorter than 12–15 amino acids. In some cases peptide
as long as 30 amino acids were needed to observe interaction.
11. It is possible to observe “a bulk shift” at the beginning of the
injection. This can be due to differences in refractive index
between the running and the sample buffer.
12. It is important to determine a most appropriate regeneration
buffer (to choose the less aggressive regeneration conditions
allowing complete dissociation of the complex without
compromising the stability/properties of the ligand). A combination of high pH and high salt concentration is often used.
13. For lipids that have transition temperatures above room temperature it is recommended to place the extruder heating block
onto a warm plate.
14. To reduce the dead volume, pass a syringe with buffer through
the extruder.
15. For those lipids that have transition temperatures above room
temperature, allow the temperature of the lipid suspension to
equilibrate with the temperature of the heating block for at
least 5–10 min.
16. Lower immobilization levels are expected with mixtures of
negatively charged vesicles compared to liposomes composed
of zwitterion lipids.
17. When using single-cycle kinetics, one can proceed with up to
five analyte (increasing concentrations) injections.
18. L1 sensor chips can be reused several times. Yet more and more
liposomes will be required to reach 4000 RUs of
immobilization.
19. Analyte can be preincubated with a fixed concentration of
peptide before being perfused over immobilized liposomes.
Acknowledgments
Work in the laboratory of PZ is currently supported by the Fund for
Scientific Research–Flanders (FWO Grants G.0846.15 and
G0C5718N), the Agence Nationale de la Recherche (ANR-18CE13-0017, Project SynTEV) and receives funding from the
European Union’s Horizon 2020 research and innovation program
under the Marie Sklodowska-Curie grant agreement no. 747025.
PDZ-Peptide and PDZ-Lipid Interactions by SPR
87
References
1. Morais Cabral JH, Petosa C, Sutcliffe MJ,
Raza S, Byron O, Poy F, Marfatia SM, Chishti
AH, Liddington RC (1996) Crystal structure
of a PDZ domain. Nature 382:649–652
2. Luck K, Charbonnier S, Trave G (2012) The
emerging contribution of sequence context to
the specificity of protein interactions mediated
by PDZ domains. FEBS Lett 586:2648–2661
3. Feng W, Zhang M (2009) Organization and
dynamics of PDZ-domain-related supramodules in the postsynaptic density. Nat Rev Neurosci 10:87–99
4. Nourry C, Grant SGN, Borg J-P (2003) PDZ
domain proteins: plug and play! Sci STKE
2003(179):RE7
5. Zimmermann P, Zhang Z, Degeest G,
Mortier E, Leenaerts I, Coomans C, Schulz J,
N’Kuli F, Courtoy PJ, David G (2005) Syndecan recycling is controlled by syntenin-PIP2
interaction and Arf6. Dev Cell 9:377–388
6. Egea-Jimenez AL, Gallardo R, Garcia-Pino A,
Ivarsson Y, Wawrzyniak AM, Kashyap R,
Loris R, Schymkowitz J, Rousseau F, Zimmermann P (2016) Frizzled 7 and PIP2 binding by
syntenin PDZ2 domain supports frizzled 7 trafficking and signalling. Nat Commun 7:12101
7. Ivarsson Y, Wawrzyniak AM, Kashyap R,
Polanowska J, Betzi S, Lembo F,
Vermeiren E, Chiheb D, Lenfant N,
Morelli X, Borg JP, Reboul J, Zimmermann P
(2013) Prevalence, specificity and determinants of lipid-interacting PDZ domains from
an in-cell screen and in vitro binding experiments. PLoS One 8:e54581
8. Ivarsson Y, Wawrzyniak AM, Wuytens G,
Kosloff M, Vermeiren E, Raport M, Zimmermann P (2011) Cooperative phosphoinositide
and peptide binding by PSD-95/discs large/
ZO-1 (PDZ) domain of polychaetoid, Drosophila
zonulin.
J
Biol
Chem
286:44669–44678
9. Mortier E, Wuytens G, Leenaerts I, Hannes F,
Heung MY, Degeest G, David G, Zimmermann P (2005) Nuclear speckles and nucleoli
targeting by PIP2-PDZ domain interactions.
EMBO J 24:2556–2565
10. Zimmermann P, Meerschaert K, Reekmans G,
Leenaerts I, Small JV, Vandekerckhove J,
David G, Gettemans J (2002) PIP2-PDZ
domain binding controls the association of syntenin with the plasma membrane. Mol Cell
9:1215–1225
11. Wawrzyniak
AM,
Vermeiren
E,
Zimmermann P, Ivarsson Y (2012) Extensions
of PSD-95/discs large/ZO-1 (PDZ) domains
influence lipid binding and membrane targeting of syntenin-1. FEBS Lett 586:1445–1451
12. Sheng R, Chen Y, Yung Gee H, Stec E,
Melowic HR, Blatner NR, Tun MP, Kim Y,
K€allberg M, Fujiwara TK, Hye Hong J, Pyo
Kim K, Lu H, Kusumi A, Goo Lee M, Cho W
(2012) Cholesterol modulates cell signaling
and protein networking by specifically interacting with PDZ domain-containing scaffold proteins. Nat Commun 3:1249
Chapter 6
PDZ Sample Quality Assessment by Biochemical
and Biophysical Characterizations
Célia Caillet-Saguy, Sébastien Brûlé, Nicolas Wolff, and Bertrand Raynal
Abstract
PDZ domains are small globular domains involved in protein–protein interactions. They participate in a
wide range of critical cellular processes. These domains, very abundant in the human proteome, are widely
studied by high-throughput interactomics approaches and by biophysical and structural methods. However, the quality of the results is strongly related to the optimal folding and solubility of the domains. We
provide here a detailed description of protocols for a strict quality assessment of the PDZ constructs. We
describe appropriate experimental approaches that have been selected to overcome the small size of such
domains to check the purity, identity, homogeneity, stability, and folding of samples.
Key words PDZ domain, Folding, Protein sample, Quality control, Biophysical techniques, Stability,
Purity, Identity, Structure, Quantification
1
Introduction
PDZ domains are one of the most abundant protein–protein interaction domains found in metazoans. The human proteome contains 266 identified PDZ domains, termed the PDZome, spread
over 152 proteins [1]. PDZ domains are mainly found in large
scaffold proteins, existing with a wide variety of modular domains.
The PDZ-mediated interactions participate in numerous critical
cellular processes and are often targeted in pathologies and by
numerous viral proteins during infection [2].
PDZs are globular domains of ~90 amino-acid residues, which
recognize PDZ binding motifs (PBMs), mainly at the extreme
C-terminus of their partner proteins. They are recognized as wellfolded and compact domains. The boundaries of most experimental
PDZ constructs that have been used for structural studies or interaction assays generally extend beyond the strict boundaries of the
Célia Caillet-Saguy and Sébastien Brûlé contributed equally to this work.
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021
89
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Célia Caillet-Saguy et al.
core PDZ structure. If PDZ domains are mainly robust, soluble
domains, inappropriate delimitations can lead to unstable protein.
Indeed, many studies have shown that extensions can influence the
dynamics, stability, and solubility of the PDZ domains [3] for often
unknown reasons. The extension of N- and C-terminus can entropically stabilize the domain affecting its internal dynamics. Thus,
the thermostability and the folding of PDZ domain are dependent
in many cases on short disordered extensions at their two termini,
but also of adjacent modules forming homotypic and heterotypic
PDZ supramodules [4]. These N- and C-terminal extensions could
form functional and structural units playing a crucial role of activity
modulation of the adjacent PDZ domain. Indeed, sequence context impacts the stability and solubility of constructs and can deeply
influence binding affinity and specificity, and structured and disordered extensions may affect the structure and function of the core
PDZ domain [5].
Optimal folding and solubility of PDZ domains are needed to
ensure accurate affinity measurement and structure determination.
Conformational exchange can also be an intrinsic property of PDZ
domains, for example with the tandem of PDZ domains in whirlin
[6] or of GRIP1 [7], and also the autoassociation capacity of the
unique PDZ domain of MAST2 (microtubule-associated serine and
threonine kinase 2; [8]) or of the second PDZ domain of ZO2
[9]. Notably, the frequency of PDZ-PDZ interactions in eukaryotic
proteins has been documented, and dimerization occurs in vitro in
30% of the 157 PDZ domains tested [10].
Altogether, these examples illustrate the need for a strict quality
assessment of the PDZ domains before deeply characterizing their
biochemical, biophysical, and functional properties. Appropriate
experimental approaches should be selected to overcome the
small size of such domains. Here, we report the contribution of a
series of biochemical and biophysical approaches applied to PDZ
domain constructs for sample quality evaluation. These analyses
were used to assay the purity, identity, homogeneity, stability, and
folding of PDZ domain construct samples. We use as an example
the PDZ domain of the kinase protein MAST2 and detail every step
of this workflow, the principles, the techniques used, and some
protocols in this chapter. The biochemical/biophysical characterizations are divided into four parts: purity, homogeneity, identity,
and conformational stability/folding state.
1.1
Purity
1.1.1 SDS-PAGE
Electrophoresis
SDS-PAGE electrophoresis (polyacrylamide gel electrophoresis
containing sodium dodecyl sulfate) is a technique that separates
proteins in a gel [11]. The migration is controlled by an electric
field, enabling their separation according to their molecular weight.
The polyacrylamide gel is made by the copolymerization of
acrylamide and bisacrylamide, in the presence of polymerization
agents (e.g., TEMED, ammonium persulfate). The concentration
PDZ Sample Quality Assessment
91
of acrylamide used controls the size of the pores; the higher the
percentage, the smaller the pore. SDS (sodium dodecyl sulfate) is a
strong detergent with a long hydrophobic hydrocarbon tail and a
negatively charged extremity. It interacts with proteins by binding
their hydrophobic regions through its hydrocarbon part. By binding to the protein, the SDS prevents its folding. The native structure of the protein is therefore denatured, and an apparent negative
charge is then conferred to the protein. In the presence of SDS, all
the proteins will, therefore, have an apparent negative charge proportional to their polypeptide chain length. This means that only
the molecular weight of the proteins will be the factor of their
separation. Using the determined molecular mass, the presence of
a given known protein will be evaluated.
1.1.2 Capillary Gel
Electrophoresis
SDS capillary gel electrophoresis (SDS-CGE), also called capillary
gel electrophoresis (CGE), is another technique to easily determine
the purity of a PDZ construct. This technique is typically used to
separate proteins according to their size in a capillary which is filled
with polyacrylamide gel and SDS. The presence of SDS aids the
electrophoretic mobility of proteins, as it coats their surface proportional to their size. Consequently, the molecular structure will
have little influence on mobility, so macromolecules will migrate
according to their molecular mass very similarly to the SDS-PAGE
technique.
CGE has many advantages over classical SDS-PAGE, including
on-capillary detection, high separation efficiency, the capability of
accurate protein quantification and molecular weight determination, and adaptability to high-throughput method with high
reproducibility.
1.1.3 UV–Visible
Spectroscopy Between
200 nm and 340 nm
Absorption spectrophotometry is the measure of the attenuation of
the light passing through a medium in order to obtain the concentrations of absorbent substances (chromophores).
PDZs can be analyzed by absorption spectrophotometry. These
biomolecules have two main absorption band in the near-UV
region and do not absorb in the visible region (400–800 nm) of
the electromagnetic spectrum. This near-UV absorption allows to
quantify PDZs and provides information about their purity.
In the absorption spectrum of protein, a maximal absorption
band can be found around 280 nm (less intense) and between
200 and 220 nm (more intense). The bands at 280 nm and
205 nm correspond to transitions π ! π* in aromatic α-amino
acids (tyrosine, phenylalanine, and tryptophan) and in amide
groups (peptide bonds), respectively. At 280 nm, tyrosine, cystine,
and tryptophan have specific molar absorptivities that allow to
calculate a molar absorption coefficient for a given protein sequence
(http://web.expasy.org/protparam/; [12]). The main contribution at 280 nm comes from the tryptophan and tyrosine with an
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absorbance of 5580 M1.cm1 and 1480 M1.cm1, respectively.
The quantification at 280 nm is therefore suitable in presence of at
least 1 tryptophan or 1 tyrosine in the PDZ sequence.
The molar absorption relates the measured absorbance to the
concentration of the protein through the Beer–Lambert law: A
(λ) ¼ ε.l.c, where A is the absorbance at a given wavelength λ
(280 nm for a protein), ε (in M1 cm1) is the molar absorption
coefficient (also called the extinction coefficient) for a given PDZ,
and c is the concentration of PDZ of interest (in M) (see Note 1).
These properties can be used in protein analysis, either to identify
protein-containing fractions or to determine the concentration of
protein in a purified sample. Quantification of PDZs in a complex
mixture (e.g., isolated fractions of cells) by UV visible absorption is
difficult since protein compositions and their absorption coefficients are not known. Absorption in the 205 nm region (peptide
bonds) can also be used for the spectrophotometric assay of proteins [13], this is of particular importance as some PDZs do not
contain tryptophan or tyrosine. For the majority of proteins,
UV-vis absorption allows to measure a mass concentration with
concentration as low as 100 μg/ml.
In addition to the measure of the concentration using the
molar absorption coefficient of the PDZ, a complete spectrum
will also inform about the general quality of the preparation. It
should be emphasized that the exact PDZ buffer should be used as
a blank to avoid misinterpretation. A strong signal at 260 nm is
usually a sign of nucleic acid contamination or small compounds
such as DTT, detergents, imidazole, ATP, etc. [13–15]. In the
presence of a 260 nm contamination, the quantification at
280 nm can easily be overestimated. In addition, a regularly increasing absorbance between 340 nm and 300 nm is generally indicative
of scattering due to aggregation, also resulting in quantification
overestimation.
1.2
Identity
1.2.1 Intact Mass
Spectrometry
Mass spectrometry is a sensitive analytical method that can be easily
implemented to determine the intact molecular mass of PDZ
domains. The combination of the small size of the PDZs and the
high accuracy in m/z of mass spectrometry devices allows, in addition to ensuring the molecular mass, to determine potential posttranslational modifications (PTMs) of the domains. Proteolytic
processing due to the loss of one or several amino acids is crucial
to monitor and nearly impossible to detect by gel electrophoresis.
In addition, the fragmentation of PDZs into small peptides allows
sequencing and thus confirms the identity of the PDZ of interest
(see in-source decay (ISD) part). Many mass spectrometer configurations have been developed, each with its strengths and limitations. In this chapter, only the MALDI-TOF configuration will be
explained.
PDZ Sample Quality Assessment
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In 1988, Karas and Hillenkamp developed a way to produce
ions from proteins with a molecular mass greater than 10 kDa [16]
by laser desorption using a matrix. As a result of this work, MALDI
(matrix-assisted laser desorption ionization) was made possible and
opened new perspectives in proteomic analysis. MALDI is a soft
ionization which is crucial to preserve intact mass of fragile and
nonvolatile compounds such as proteins. The principle of this
ionization mode is based on the irradiation by laser pulses of a
crystalline phase containing the cocrystallized PDZ and an organic
matrix. This irradiation is carried out using a laser pulsed usually at a
UV wavelength, which causes the desorption of the PDZ–matrix
pair that is charged by the transfer of protons from the matrix to the
analyte. Importantly, the sample must be able to release or capture
protons (transfer with the matrix), depending if the positive or
negative ionization mode is used to ionize the sample. For proteins,
a linear and positive mode is usually applied and the matrix must
absorb within the laser emission range to allow the sample to
vaporize. The ions are then accelerated by the application of an
electrical pulse and pass through a separation tube, called Time of
Flight (TOF), maintained under vacuum. As the difference of
potential is constant for all ions, ions with a smaller m/z value
(lighter ions) move faster until they reach the detector. Therefore,
the time of flight is different depending on the mass to charge ratio
(m/z). The signal at the detector is recorded and amplified and then
processed electronically to generate a m/z spectrum. In MALDITOF, proteins mainly result in singly charged or doubly charged
ion species.
One of the main advantages is that the sample droplets can be
prepared rapidly on the metal target, allowing the analysis of a large
number of proteins in a short time. Also, nowadays, most of the
scientific centers have access to this technology and research teams
can easily be trained to complement their SDS-PAGE
electrophoresis.
1.2.2 Top-Down PDZ
Sequencing
Top-down sequencing (TDS) with MALDI is a complementary
application of trypsin digestion. The generated trypsin digestion
peptides are analyzed and compared to queries databases to determine a protein identity from SDS gel bands or liquid samples. The
objective of the TDS strategy is to ensure that the protein sample is
the expected one by comparison of both the N- and C-termini
sequences with the generated sequencing, assuming no degradation or enzymatic cleavage. This technique allows researchers to
work directly on intact and undigested PDZs. TDS is truly suitable
for the analysis of small domains such as PDZs. The fragmentation
is done directly in the source (in-source decay or ISD) using a laser
power much higher than the one used for intact mass. In practice,
ISD fragmentation, compared to collision-induced fragmentation
(CID), triggers a much more independent fragmentation of the
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protein sequence. Depending on the matrix, ISD fragments are
mainly b-, c-, y-, and z- ions. The 2,5-dihydroxybenzoic acid
(DHB) and super DHB matrices contribute to the N-terminal
validation, while the 1,5-diaminonaphthalene (DAN) matrix helps
in determining the C-terminal residues. Compare with the Edman
degradation, ISD has the advantage to detect both termini and in
presence of modifications. This method can confirm 10–50 residues from both sides of the intact protein. Due to the matrix
contribution below 800 Da, the first residues cannot be determined. This weakness can be overcome by using the
T3-sequencing approach or Pseudo-MS3, where selected ISD fragments are further fragmented (MALDI-TOF/TOF) [17]. Nowadays, the all procedure intact mass and T3-sequencing can be
performed in less than 1 h.
Homogeneity
By PDZ domain homogeneity measurements, we intend to measure if the domain is monomeric or oligomeric, the state of assemblies, and if the sample contains soluble high-order assemblies and
aggregates. There is no single technique that will answer all these
issues [18], as one of the challenges is the concentration required to
make the measurement given the small molecular mass of PDZ
domains.
1.3.1 Dynamic Light
Scattering
Dynamic light scattering (DLS), because of its speed and low
sample consumption, is a very convenient method to determine
the monodispersity of the species of interest and the presence of
soluble high-order assemblies and aggregates, simultaneously
[19]. However, for PDZ domain measurement, a concentration
of at least 1 mg/ml is needed to get a reliable calculation of the
hydrodynamic radius (Rh) of the domain. It is important to emphasize that DLS does not measure molecular mass, but Brownian
motion, which is related to the Rh of the particles. With the
translational diffusion coefficient deduced from this motion, the
hydrodynamic radius can be calculated, that is, the radius of the
sphere that would diffuse with the same rate as the molecule of
interest [20]. One should note that light scattering intensity is
related to the cube of the radius, and consequently, DLS is the
method of choice to detect small quantities of large aggregates in a
PDZ sample [18]. This property makes it difficult to estimate the
relative amount of the detected specie with the added difficulty that
DLS has a weak resolving power and cannot distinguish between
protein monomer and dimer in such molecular weight range, that is
10–20 kDa (Fig. 1).
1.3.2 Analytical Size
Exclusion Chromatography
To further analyze the oligomeric states of the PDZ domains, DLS
measurement can be completed by analytical size exclusion chromatography (SEC). SEC is currently the standard separation technique to quantify protein oligomers. It separates molecules
1.3
PDZ Sample Quality Assessment
95
Fig. 1 Hydrodynamic radius distribution, measured by DLS. Inset autocorrelation function of the scattered
intensity
according to their Stokes or hydrodynamic radius [21], with larger
molecular species eluting before smaller ones. Aggregates, contaminants, and potentially different molecular arrangements of
the protein of interest can be readily separated and quantified,
with an online concentration detection (UV or refractive index
(RI)). However, the precise quantification of the molecular mass
of each eluted species requires an online static light scattering (SLS)
detector [22], in combination with the UV or RI detector (Fig. 2).
Several types of SLS systems are available on the market with multiangle detectors or MALS (multiangle light scattering), and low
angle detectors. Their main differences are centered around the
way they extrapolate light scattering intensity at 0 to measure
molecular mass [20]. In the case of PDZ domains, both methods
will give the same result as the domain is small and the instrument
will not detect any angular dependence. However, the protein
sample will be diluted during the SEC experiment by as much as a
ten-fold factor that might alter the equilibrium between oligomeric
species. Furthermore, “inert” may the gel filtration resins be, some
proteins do interact with them, rendering SEC impossible. Finally,
for small domains such as PDZs, the gel filtration column may not
be resolute enough to separate different oligomeric states that
differ by only 10 kDa. In all these cases, the technique of choice
to measure molecular mass will be analytical ultracentrifugation.
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Célia Caillet-Saguy et al.
Fig. 2 Molecular mass and intrinsic viscosity measurement of MAST2 after size exclusion chromatography
separation. Data were collected by light scattering measurement at 90 C and the determined molecular mass
showed the presence of monomer with an intrinsic viscosity of 4.2 dl/g
1.3.3 Analytical
Ultracentrifugation
During analytical ultracentrifugation experiment, there are no dissociating forces acting on the sample and the concentration is
constant during the all-time. However, it is more sample- and
time-consuming than SEC SLS and will require a specialist help.
An analytical ultracentrifuge allows the monitoring of the sedimentation process of a sample while moving in the centrifugal field
[23]. It will separate PDZ domains according to their sedimentation features which are linked to their size and molecular mass.
Sedimentation velocity experiments are done at high speed to
analyze the movement of the sedimenting species until all sample
collects at the bottom of the cell. Then, the experiments are analyzed using a specific algorithm such as the one used in SEDFIT and
Ultrascan [24, 25] to determine, for each species present, a sedimentation coefficient, a frictional ratio and consequently a molecular mass (Fig. 3). For each species, we will get macromolecular
shape information as well as the oligomeric state. The resolving
power of the experiment will allow to separate contaminants and
aggregates and the different oligomeric states of the PDZ. Nowadays, the detectors are really versatile and can detect sample absorption at any wavelengths between 190 nm and 600 nm, or refractive
index changes allowing measurement of PDZ samples at concentration as low as 10 μg/ml. We will not detail the technique in this
chapter. However, a good starting point for new users is the technical paper of Andrea Balbo and al [26]. The techniques will use
between 100 and 400 μl of sample at a concentration ranging from
PDZ Sample Quality Assessment
97
Fig. 3 Sedimentation coefficient distribution of MAST2. Two species can be
noticed with sedimentation compatible with a main peak of monomer and some
dimer formation in the second peak
0.1 to 3 mg/ml. Data can be analyzed with SEDFIT or Ultrascan
algorithms [25, 27] using the most adapted model such the continuous size distribution c(S) model presented in the figure for
MAST2-PDZ (Fig. 3).
1.4 Conformational
Stability/Folding State
To verify that the same folding signature can be seen for different
batches of PDZ, one of the classical technique is circular
dichroism (CD).
1.4.1 Circular
Dichroism (CD)
Circular dichroism (CD) relies on the ability of a sample having a
chiral chromophore or placed in an asymmetric environment, to
absorb differently circularly polarized right light and circularly
polarized left light. The spectrum of circular dichroism corresponds
to the difference of absorbance between these two polarizations of
light, for each of the wavelengths. Studying macromolecules by CD
allows to obtain information about their folding. For proteins, far
UV (180–260 nm) and near UV (250–330 nm) circular dichroism
measurements give insight respectively into their secondary structure content and their tertiary organization, respectively. The
far-UV CD spectroscopy of proteins and peptides is predominantly
based on the excitation of electronic transitions in amide groups.
Two types of electron transitions are responsible for the CD signals
in this wavelength region, an n ! π* transition at around 222 nm,
and π ! π* transitions (both parallel and perpendicular orientations) at 208 and 190 nm. The peptide backbone forms
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Célia Caillet-Saguy et al.
1
0
Δε (104 deg cm2 dmol–1)
-1
-2
-3
-4
-5
-6
-7
-8
195
205
215
225
Wavelength (nm)
235
245
Fig. 4 Circular dichroism spectra of MAST2-PDZ. Spectra of the free MAST2PDZ, the free peptide and the complex MAST2-peptide are shown in blue,
orange, and gray respectively
characteristic secondary structures such as α-helices, β-sheets,
turns, and disordered sections with specific Φ, Ψ dihedral angles
and H-bond patterns affecting the CD spectrum. Numerous algorithms have been developed for the quantitative estimation of the
secondary structure composition from the CD spectra with a good
overall prediction [28]. For PDZ domains, the CD spectrum is a
linear combination of the spectra of its secondary structural elements that are mainly 1 to 2 α-helices, a β-sheet (5 to 6 strands),
and turns (Fig. 4). In the near-UV wavelength region, the major
chromophores generating CD features of proteins are the aromatic
side chains of tryptophan, tyrosine, and phenylalanine together
with the disulfide bonds. Only qualitative tertiary structural information can be obtained from the environmental-dependent CD
spectra of protein aromatic residues in the near UV wavelength
range.
CD has been used for protein folding assays, intermolecular
interactions and in the investigations of protein disorder. It is one of
the easiest and sensitive methods to detect conformational changes
that might result from changes in pH, salt concentration, ionic
strength, added solvents, or mutations in native protein, to assess
the thermal or chemical stability by following molecule unfolding,
and to analyze protein–ligand interactions.
1.4.2 Differential
Scanning Calorimetry
(DSC) and Differential
Scanning Fluorimetry (DSF)
Delimitations of PDZ domain limits are tricky to define. Stability,
folding, and dynamics of PDZ constructs need to be optimized.
High thermostability is an important feature to improve PDZ
behavior during crystallization [29–31] or other structural studies
PDZ Sample Quality Assessment
A
99
Cp
(kcal/mol/°C)
3
Cp (10 kcal/mole/°C)
3000
47°C
2500
47°C
2000
1500
1000
45°C
41°C
49°C
52°C
500
0
First derivative
B
23 25 28 30 32 34 37 39 41 43 45 48 50 52 54 57 59
T (°C)
T (°C)
47.1°C
55.5°C
61.7°C
60.9°C
56.4°C
T (°C)
Fig. 5 Thermal stability of PTPN3-PDZ and MAST2-PDZ followed by DSC and
DSF, respectively. (a) DSC thermograms of PTPN3-PDZ unbound (black line) and
complexed to PBM peptides (dashed lines). (b) By calculating the maximum of
the first derivative of the ratio of fluorescence (F) at 350 nm over 330 nm, the
melting temperature (Tm) can be derived; it corresponds to the temperature
where 50% of the proteins are unfolded (blue line for MAST2-PDZ unbound and
green/red lines for MAST2-PDZ complexed to PBM peptides)
[32]. Several techniques can be used to monitor the thermostability
of the sample such as: CD coupled to a temperature gradient that
monitors the loss of secondary structure as a sign of global unfolding [29], DSF that detects changes in the tryptophan and tyrosine
environment, as well as thermofluor and DSC [33]. All these
approaches are based on partial or full unfolding of proteins during
heat denaturation. These techniques use different observable data
to monitor protein unfolding that could lead to a sharp modification of the signal over a short temperature range. The melting
temperature (Tm) that corresponds to the temperature where the
protein is 50% unfolded (Fig. 5) can be derived by calculating the
peak of the first derivative.
A biomolecule in solution is in equilibrium between its native
(folded) and denatured (unfolded) conformations. A higher thermal transition midpoint (Tm) corresponds to a more stable molecule. DSC measures the enthalpy (ΔH) of unfolding that results
from heat-induced denaturation. It is also used to determine the
change in heat capacity (ΔCp) of denaturation. The combination of
ΔH, ΔCp and Tm helps to define the intrinsic properties of the
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protein, in our case a PDZ domain. As an example, some proteins
may have a low Tm associated with a large ΔCp which will confer a
great stability in vivo. In parallel, PDZs with a mass > 5000 Daltons, such as proteins, form well-defined structures that undergo
thermally induced conformational changes [34]. These structural
rearrangements result in the absorption of heat caused by the
redistribution of noncovalent bonds. Differential scanning calorimeters measure this heat uptake.
Concerning NanoDSF, this technique records the intrinsic
fluorescence of Tryptophan (Trp) and Tyrosine (Tyr) residues,
which are very sensitive to changes in their local environment.
Thermal unfolding is measured by monitoring the intrinsic Trp
and Tyr fluorescence intensity, and the position of the emission
maximum as a function of temperature. The fluorescence intensity
ratio between 330 and 350 nm is defined as an empirical parameter
to monitor the evolution of the microenvironment of the aromatic
residues during protein denaturation throughout the temperature
increases. This ratio sharply increases/decreases during thermal
unfolding, allowing to determine a Tm value [35]. The applicability
of nanoDSF is highly dependent on the presence of Trp and Tyr in
the folded core of the PDZ that are exposed upon unfolding.
Moreover, it is necessary to exclude that the observed signal
changes are caused by aggregation, as this will also lead to variations
in the fluorophore environment. A back-scattering measurement
can also be performed to determine if aggregation occurs (before or
concomitantly with the denaturation). The characteristics of the
temperature gradient are essential as it is related to the activation
energy via the Arrhenius equation [6]. Typically, a heating rate of
1 C/min is applied. Overall, comparing the melting temperatures
(ΔTm) in different buffer compositions allows researchers to define
the optimal buffer condition as an increase in Tm corresponds to a
better thermal stability and to a reduced conformational flexibility.
1.4.3 Nuclear Magnetic
Resonance (NMR)
NMR is based on the measure of the absorption of radiofrequency
(RF) radiation by an atomic nucleus located in a strong magnetic
field. The principle of NMR is that atomic nuclei, with an odd
number of protons (1H, 13C, 15N, 31P, . . .), neutrons, or both,
have an intrinsic nuclear spin. When an atomic nucleus with a
nonzero spin is placed in a magnetic field, the nuclear spin aligned
in the same direction or in the opposite direction to the field.
Different energies characterize these two types of nuclear spin
alignment, and the application of a magnetic field facilitates the
degeneration of nuclear spins. An atomic nucleus whose spin is
aligned with the field will have less energy than when its spin is
aligned in the opposite direction of the field.
The energy of an NMR transition depends on the magnetic
field strength as well as on the proportionality factor applied to each
nucleus called the gyromagnetic ratio. The local environment
PDZ Sample Quality Assessment
101
around a given nucleus in a molecule tends to slightly disturb the
local magnetic field exerted on that nucleus and to affect its transition energy. This dependence of the transition energy on the position of a particular atom in a molecule makes NMR extremely
useful for determining the structure of molecules.
The sample placed in an intense magnetic field will be disturbed
by radiofrequency pulses. The recording of the return to the equilibrium of the spins makes it possible to have access to the chemical
environment of the atoms. This information offers the possibility of
structural and dynamic analyzes as well as the study of interactions
involving biological macromolecules.
NMR can be used to characterize the degree of folding of a
protein by observing the dispersion of the resonance peaks. Indeed,
in the one-dimensional (1D) 1H spectrum or the two-dimensional
(2D) 1H-15N correlation spectra if the protein is 15N-labeled, the
peaks for a well-folded protein are narrow and sharp and distributed
over a large range of chemical shifts (good signal dispersion)
(Fig. 6). 1H resonances can be especially found at values
<0.5 ppm (corresponding to high field-shifted methyl group protons) or > 8.5 ppm (corresponding to down field-shifted amide
protons). In contrast, the peaks can be broader and not as widely
dispersed in the spectrum of an unfolded or partially folded protein.
Moreover, observed linewidths of peaks are related to the molecular
weight of the protein, and then may be indicative of autoassociation
or aggregation. In addition to the evaluation of the folding and the
stability of a protein, a 1D 1H spectrum provides information about
purity. Indeed, impurities with low molecular weight and observable nuclei give rise to sharp signals amongst the broader envelope
of the protein resonances.
The most common 2D spectrum to obtain structural information about a protein is the 1H-15N heteronuclear single quantum
coherence (HSQC) spectrum. It correlates the nitrogen atom of an
amide group with the directly attached amide proton. Since there is
only one backbone HN per amino acid, except for Pro, each HSQC
signal represents one single amino acid. The HSQC also contains
signals from the NH2 groups of the side chains of asparagine and
glutamine (also lysine and arginine depending on the pH values)
and of the aromatic HN protons of Trp and Histidine. The signals
may cover a spectral range from 6.0 to 12 ppm. If a protein is
folded, its signals are distributed over the complete spectral range;
if not, signals are located between 7.5 and 8.5 ppm in the proton
dimension. Signals outside these regions indicate that the protein is
folded and in a defined three-dimensional (3D) state. The chemical
shift is very sensitive to the overall structure so that even slight
conformational changes will affect the signals in a 1H-15N HSQC
spectrum, making it a very efficient tool to check conformational
stability and folding states.
Célia Caillet-Saguy et al.
A
Downfield
backbone NH
11
10
9
Downfield
Hα
8
7
6
5
Upfield
CH3
4
3
2
1
0
-1
-2
ppm
1H (ppm)
B
105
110
15N (ppm)
102
115
120
125
130
10.0
9.5
9.0
8.5
8.0
1H (ppm)
7.5
7.0
Fig. 6 NMR spectra of MAST2-PDZ. (a) 1H 1D spectrum of 150 μM MAST2-PDZ
in complex with a viral peptide at 600 MHz 1H frequency. (b) 1H-15N 2D HSQC
spectra of 150 μM MAST2-PDZ alone (in black) and in complex with a viral
peptide (in red) at 600 MHz 1H frequency
PDZ Sample Quality Assessment
2
Materials
2.1
Purity
2.1.1 Basic Reagents
for SDS-PAGE
Electrophoresis
103
1. Electrophoresis tank.
2. Power supply.
3. Polyacrylamide gels. Novex™ 4–12% Tris-Glycine Mini Gels
offers a broad range of molecular weight resolution from 6 to
400 kDa. Standard 10–12% polyacrylamide gels can also be
used to resolve PDZ domains.
4. 2 Sample buffer: 100 mM Tris–HCl pH 6.8, 0.2% Bromophenol Blue (BBP), 4% SDS, 20% glycerol, 200 mM dithiothreitol (DTT) (see Note 2). Add 1 ml of 1 M Tris pH 6.8, a
pinch of BromoPhenol Blue (BPB) and vortex, 4 ml of 10%
SDS, 2 ml of 100% glycerol, and 2 ml of 1 M DTT.
5. Molecular-weight markers such as PageRuler™ Prestained
Protein Ladders from 10 to 180 kDa.
6. Migration buffer such as NuPAGE MES SDS Running Buffer.
Add 50 ml of the NuPAGE™ MES SDS Running Buffer (20)
and complete to 1 l with pyrolysis water.
7. Home-made or commercial protein staining solution. InstantBlue is a ready to use Coomassie protein stain for
polyacrylamide gels.
2.1.2 Capillary
Electrophoresis (CE)
1. Capillary electrophoresis system in this case a LabChip GX II
(PerkinElmer).
2. LabChip HT Protein Express Chip (PerkinElmer, 760499).
3. Protein Express
CLS960008).
Assay
Reagent
Kit
(PerkinElmer,
4. HT Protein 200 Sample Buffer (PerkinElmer 760518).
5. 384-well PCR plate (Greiner Bio-One) (see Note 3)
2.1.3 UV Spectroscopy
1. UV-Vis spectrometer.
2. Quartz cuvette suitable for UV (190–400 nm).
3. Sample buffer as described in Subheading 2.2, item 4.
4. Special wipe such as Kimwipes to clean the cuvette before
measuring.
2.2 Integrity
Measurements by
MALDI-TOF
2.2.1 Total Mass
1. MALDI steel plate.
A freshly prepared matrix solution: 25 mg/ml α-cyano-4hydroxycinnamic acid (HCCA) in 50% (v/v) acetonitrile,
0.1% (v/v) trifluoroacetic acid (TFA) in HPLC water.
2. ZipTip with C4 resin (Merck Millipore).
3. High quality protein standard from Bruker, Laserbio Labs, or
Sciex.
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Célia Caillet-Saguy et al.
2.2.2 ISD and T3
Sequencing
1. MALDI steel plate.
2. Two matrix solutions: 25 mg/ml 1,5-diaminonaphthalene
(DAN) and 25 mg/ml of a mixture composed of
2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic
acid (super-DHB). Both matrices are dissolved in 50% Acetonitrile (v/v), 0.1% (v/v) TFA in HPLC water.
3. ZipTip with C4 resin (Merck Millipore).
4. 1 mg/ml BSA in PBS as ISD standard.
2.3
Homogeneity
2.3.1 Dynamic Light
Scattering (DLS)
1. 0.22 μM Syringe filter.
2. Reusable quartz cuvettes (Hellma) or plates depending on the
model.
3. Detergent solution: 2% (v/v) Hellmanex.
4. Buffer used for PDZ sample: 1 ml.
5. PDZ sample.
2.3.2 Size Exclusion
Chromatography (SEC)
with Multi Angle Light
Scattering (SEC-MALS)
1. Size exclusion column (e.g., Superdex 75 increase from GE
healthcare).
2. PBS (137 mM [NaCl], 2.7 mM [KCl], 10 mM [Na2HPO4],
1.8 mM [KH2PO4]) (1 l) or other buffer (see Note 4).
3. PDZ sample. Prepare your sample in the chosen buffer with a
protein concentration at least of 1 mg/ml. This is particularly
true for PDZ domain if you want sufficient signal on the light
scattering detector as the signal is proportional to the molecular mass of the protein.
2.4 Conformational
Stability/Folding State
1. Aviv 215 spectropolarimeter.
2.4.1 Circular
Dichroism (CD)
3. PDZ sample diluted at 0.2 mg/ml with PDZ sample buffer.
2. 0.2 mm path-length cylindrical cell (Hellma) (see Note 5).
4. PDZ sample buffer.
5. 70% ethanol.
6. 2% (v/v) Hellmanex.
2.4.2 Differential
Scanning Fluorimetry
(Nano DSF)
1. Prometheus NT.48.
2. 70% ethanol to clean the lens.
3. Quartz capillary.
4. Buffer used for PDZ domain preparation and storage.
5. PDZ sample diluted at 0.2 mg/ml with PDZ sample buffer.
6. Peptide solubilized in PDZ sample buffer (tenfold molar excess
relative to PDZ sample).
PDZ Sample Quality Assessment
105
1. 600 MHz (or higher fields) NMR spectrometer equipped with
triple resonance 1H{13C/15N} PFG probe (for heteronuclear
NMR detection).
2.4.3 NMR
2. 4 mm NMR tube (Shigemi INC).
3. 100 μM PDZ sample (250 μl).
4. 100% D2O to add to the PDZ sample at 1–5% final
concentration.
3
Methods
We used the PDZ domain of the human kinase MAST2 (UniProtKB Q6P0Q8) as a practical model. This construct of 96 residues (MAST2-PDZ) (Fig. 7a) has a molecular weight of
10435.9 Da.
3.1
The objective is to quantify the PDZ construct of interest and assess
the presence (and level) of contaminants using techniques that are
available to most laboratories.
Purity
A simple protocol to run an SDS-PAGE is given below:
3.1.1 SDS-Page
1. Prepare 20 μl of sample by adding 10 μl of 2 denaturing
loading buffer to 10 μl of protein and heat sample at 95 C
for 5 min (see Note 6).
2. Prepare the precasted gel by removing the comb and the white
tape near the bottom of the gel cassettes and place the gel in the
mini gel tank (see Note 7).
A
B
MAST2-PDZ
NH2GGSMRPPIIIHRAGKKYGFTLRAIRVYMGDSDVYTVHHMVWHVEDGGPASEAGLRQ
GDLITHVNGEP VHGLVHTEVVELILKSGNKVAISTTPLENCOOH
MW
C
MW
98 kDa
62 kDa
49 kDa
38 kDa
28 kDa
17 kDa
14 kDa
6 kDa
Fig. 7 Sequence and gel SDS-PAGE of MAST2-PDZ. (a) Primary sequence of MAST2-PDZ. (b) The SDS-PAGE
gels of a GSTrap chromatography (left panel) and a size-exclusion chromatography (right panel) of the eluted
fractions containing MAST2-PDZ
106
Célia Caillet-Saguy et al.
3. Fill the chambers with running buffer.
4. Load samples and ladders in the appropriate wells.
5. Run the gel following the manufacturer’s instructions (see
Note 8).
6. After electrophoresis, remove the gel from the tank and transfer
directly into the InstantBlue staining solution. Be sure that the
gel moves freely in stain to facilitate diffusion. Typically, about
20 ml is needed to cover the gel.
7. Colored proteins bands will start to develop immediately and a
suitable intensity is typically achieved after 15 min incubation at
room temperature with gentle shaking.
The protein sample is considered pure when no other band
other than the expected one for your protein of interest is detected
by SDS-PAGE using sensitive staining [20].
In the case of MAST2-PDZ, the protein is purified as previously described [36]. The 2-step purification process improved
drastically the purity of the sample as assessed by SDS-PAGE in
Fig. 7b. The GSTrap column allowed to remove a high content of
contaminants and to concentrate the MAST2-PDZ construct. The
SDS-PAGE gel of the eluted fractions showed a major band of the
protein and only a few contaminants extra-bands (Fig. 7b left
panel). Fractions containing MAST2-PDZ are pooled and then
purified by size-exclusion chromatography and the only band
remaining on the corresponding gel SDS-PAGE is the MAST2PDZ construct. A very weak band around 30 kDa slightly overlap
with the first fractions of MAST2-PDZ. The pool of fractions of gel
filtration is then concentrated and the gel SDS-PAGE revealed one
unique band indicative of the high degree of purity by SDS-PAGE
technique. This construct has a migration that does not match with
its calculated molecular weight as already observed. This might be
due to the effect of SDS binding [37] and/or the hydrophobicity
of the construct [38]. The limits of this technique are that the
detection is restricted to protein contaminants, and the detection
is proportional to the sample loading and gel staining and should be
confirmed by mass spectroscopy.
3.1.2 Capillary Gel
Electrophoresis (CGE)
Capillary gel electrophoresis (CGE) separates proteins according to
their molecular mass similarly to SDS-PAGE, and this methodology has been previously exploited to validate bacterial expression of
a library of 266 known human PDZ constructs developed for the
high-throughput “holdup” chromatographic assay for the determination of PDZ-PBM affinities [39]. Below we outline the main
steps of this high-throughput protocol.
1. Dilute to 1:8 (v:v), 1:16 (v:v), and 1:32 (v:v) each PDZ supernatant to be quantified.
PDZ Sample Quality Assessment
107
2. Dispense 5.6 μl/well of HT Protein 200 Sample buffer in as
many wells of a 384 well plate as required.
3. Add 4 μl/well of the PDZ dilutions to each well.
4. Boil the samples at 95 C for 5 min in a dry bath.
5. Add 20.6 μl/well of distilled H2O.
6. PDZ samples within the 384 well plate is then analyzed with
the High Sensitivity HT Protein Express protocol
(10–100 kDa program) with the LabChip GXII device (PerkinElmer) following supplier’s instructions. This technology
allows to electrophoretically separate, stain, destain, detect by
laser-induced fluorescence and analyze the protein samples.
The data analysis provides protein concentration, molecular
weight sizing, and purity evaluation using ladder and marker
calibration standards. With sample acquisition time of about
40 s, the instrument takes approximately 4 h to analyze
384 protein samples.
7. The LabChip GX software is then used for data analysis. This
allows to visualize results via an electropherogram or virtual gel
view (Fig. 8) or in a tabular form to export into a spreadsheet
format. At the end of the run, the concentrations of the initial
cultures of the soluble PDZ are calculated from the concentrations determined per PDZ in each serial dilution. The PDZ
lysate concentrations range from 10 μM for the least concentrated and up to 100 μM or higher. The concentration of
lysozyme is constant and used as an internal reference for
quantification. A limitation to the quantification is reached
when the construct is poorly expressed and falls in the background of the Escherichia coli proteins in the case of a lysate. On
the contrary, the electropherogram allows to easily detect contaminants (Fig. 8). This CGE approach is fast, efficient and
allows high resolving separations with low solvent consumption and minimal operating cost considering the high number
of samples. However, the LabChip GXII system is not common
in most laboratories. Its running cost, the short-use warranty of
ships and kits, can constitute a limitation. During the 4 h run, a
diminution in data quality may happen due to samples drying
into the 384 well plate.
3.1.3 UV Spectroscopy
UV spectroscopy between 220–240 nm and 340 nm is a good
quality test to determine protein concentration (using A280nm)
and to detect aggregation and molecule contamination. Indeed,
UV-visible spectroscopy can detect the presence of large particles
such as aggregates (radius higher than 200 nm) in a protein preparation by monitoring the absorbance signal above 320 nm, where
aggregate-free protein samples are not supposed to absorb light. If
the signal increases as the wavelength diminishes between 340 nm
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Célia Caillet-Saguy et al.
Fig. 8 Interface of the data analysis software LabChip GX reviewer. Electropherogram (top) and virtual gel view
(bottom) of the serial dilution of the supernatant of lysed cell extract of MBP-tagged MAST2-PDZ construct of
the PDZome library. The concentration of lysozyme is constant and used as an internal reference for
quantification. The peak corresponding to the PDZ construct is decreasing upon increasing dilution factor
λ (nm)
λ (nm)
Fig. 9 UV spectra of two MAST2-PDZ samples after purification
and 320 nm, it can be attributed to the scattering of light by large
aggregates present in the sample. In Fig. 9, the two UV spectra of a
sample of MAST2-PDZ show a difference in the absorbance above
320 nm, indicative of a difference in the presence of aggregates.
The aggregation index (AI) can be calculated: AI ¼ 100 A340nm/
(A280nm A340nm) with A280nm and A340nm, the absorbances at
280 and 340 nm, respectively. AI <2 is indicative of a homogeneous sample with no sign of aggregation.
PDZ Sample Quality Assessment
109
UV-visible spectroscopy is also useful for the detection of
nonprotein contaminants by monitoring the absorbance over a
large range (at least 240–340 nm). Contaminating nucleic acids,
reducing agents and detergents may show extra absorbance around
260 nm. The contribution of these contaminants is evaluated with
the ratio A260nm/A280nm, which should be lower than 0.6 [14]. We
already observed such contaminations with the first PDZ domain of
Whirlin, a protein involved in the human auditory system, which
presents a cationic patch at the surface of its PDZ1 domain. This
basic cluster of three or four Arg or Lys close to the carboxylate
binding site is common to a subset of PDZ domains that bind lipids
[40]. More generally, we might presume that PDZ domains known
to interact with lipids [41] may also interact with nucleic acids
through the cluster of basic residues. In line with this, samples
with PDZ domains having high isoelectric point (pI) values are
prone to nucleic acids contamination.
If the sample shows no sign of contamination or aggregation,
the sample concentration can be calculated using A280nm, the protein molar absorption coefficient at the working wavelength and the
Beer–Lambert’s law [42]. However, PDZ domains are small
domains and some of them lack of tryptophan. In that case, a
weak A280nm is obtained and might alter the precision of the
concentration. In that case, an alternative way is to use A205nm to
determine concentration [13].
The protocol for UV spectrum measurement comprises a run
of the baseline/blank with buffer and ones with the protein solution. The exact same buffer that was used to prepare the protein
solution should be used, ideally the buffer from the last step of
purification, that is, the exclusion chromatography in the case of
MAST2-PDZ. For concentration measurement, the absorbance at
the wavelength used must be below the specified saturation range
of the instrument to keep the linear relationship between the
absorbance and the concentration. If the absorbance is too high,
the sample must be diluted and the measurement should be
repeated. PDZ spectra are also checked for aggregation around
320–340 nm and for contamination by nucleic acids at 260 nm,
for example. If any signs of aggregation or contaminants are
detected, the A280nm and the corresponding extinction coefficient
are used to calculate the concentration of the protein in the cuvette.
A simple protocol to run a UV-visible spectrum is given below:
1. Turn on the UV-Vis spectrometer and allow the lights to warm
up for an appropriate period of time (approximately 20 min) to
stabilize.
2. Fill a cuvette with the buffer used to generate the protein
sample and make sure the outside is clean. This will serve as a
blank and help account for slight losses due to diffusion or
absorption by the solvent.
110
Célia Caillet-Saguy et al.
3. Place the cuvette in the spectrometer (see Note 9). Make sure
to align it correctly.
4. Read for the blank assay in the range of 200–500 nm for
example for a protein sample (see Note 10). Absorbance should
be minimal, but any absorption should be deducted from
future samples. Most instruments store empty data and perform subtraction automatically.
5. Fill the cuvette with the sample. To ensure that the transfer is
quantitative, rinse the cuvette 3 times with water, then 3 times
with alcohol and dry with pressured air. Make sure the outside
is clean of fingerprints by cleaning the cuvette with a
special wipe.
6. Place the cell in the spectrometer in the correct direction. Close
the cap to avoid any ambient light and collect an absorbance
spectrum in the range of 200–500 nm.
3.2 PDZ Mass
Integrity by
MALDI-TOF
There are many ways to prepare deposits on the MALDI target with
variations in terms of the matrix concentration, the solvent used, or
the crystallization process. The purpose of the droplet preparation
on the MALDI plate is to allow the protein–matrix mixture to be
close enough to cocrystallize. The dried droplet method is the most
commonly used technique because it is swift and straightforward to
implement. There are several alternatives to proceed that are all
roughly equivalent. Indeed, it is possible to directly mix the PDZ
domain with the matrix on the MALDI plate or to premix it in a
tube and deposit the mixture on the target.
3.2.1 Total Mass
Measurement
For total mass measurement of PDZ domain (Fig. 10), the protocol is as follow:
1. Use the dried droplet method: Apply 1 μl of PDZ sample on
the plate and overlay with 1 μl of HCCA. For a PDZ domain of
15 kDa, 1 μl at 0.1 mg/ml is sufficient. For a 30 kDa PDZ
construct (a PDZ tandem for example), a concentration of
0.3 mg/ml is recommended. These concentrations are acceptable for protein samples in PBS-type buffer. For complex buffer
or low PDZ quantity see Note 11.
2. Let the cocrystals air-dry for 15 min.
3. Acquire the data in linear and positive mode on the MALDITOF mass spectrometer. Use the minimum laser power
required to get a nice spectrum (see Note 12).
4. Calibrate the experiment with a standard mixture, for instance,
the Protein Standard I from BRUKER company.
5. Analyze the data (see Note 13).
PDZ Sample Quality Assessment
111
Fig. 10 MALDI-TOF spectra for two PDZs. In black, singly and doubly charged
ions of an intact PDZ; in red, several peaks showing the proteolysis of the PDZ
that cannot be easily identified on an SDS-gel
3.2.2 ISD and T3
Sequencing
The protocol to perform ISD acquisition is:
1. Use the dried droplet method: Apply 1 μl of PDZ sample on
the plate and overlay with 1 μl of super-DHB matrix. Do a
second spot with the DAN matrix (see Note 14).
2. Select an ISD method usually between 1 and 10 kDa in reflectron and positive mode. In that range of m/z, avoid any contaminations such as detergents or PEG.
3. Increase by 10% the laser power required compare to the intact
mass of the PDZ of interest.
4. Acquire at least 10,000 shoots.
5. Save your spectrum and calibrate it with BSA ions generated in
that range.
6. Open the spectrum in Biotools or another software and assign
it as an ISD-type.
Load the amino-acid sequence and analyze the data as
illustrated in (Fig. 11)
7. If the first amino acid needs to be determined, proceed with a
T3 sequencing procedure that uses a LIFT method as
described in the following steps. Define an ISD fragment ion
around 1000 m/z as parent. Copy the value of the parent ion.
8. Open the LIFT method. Paste the peak around 1000 m/z in
the LIFT method. Acquire the signal of the parent ion. Acquire
signals of the fragment ions on the parent ion spectrum. Gain
112
Célia Caillet-Saguy et al.
Fig. 11 Top-down sequencing of MAST2-PDZ. More than 10 and 20 amino acids match in C- and N-termini,
respectively
and laser power are critical issues as the sensitivity is low. Save
the data and annotate manually the ions. A search using Mascott software is also possible.
Homogeneity
The objective is to assess if the sample has a tendency to form
aggregates and to consider the potential for oligomerization of
the protein sample.
3.3.1 Dynamic Light
Scattering (DLS)
DLS experiments can be performed using nearly any buffer (see
Note 15). Special care should be taken during measurement if the
buffer contains large scattering particles such as crowding agents,
etc. The required protein concentration depends on the molecular
mass of the protein and can be estimated by the rule of thumb given
in Eq. 1.
mg
15
c
ð1Þ
¼
MW
ml
3.3
where MW is the molecular mass of the target protein in kDa.
For most PDZ domains, a concentration around 1 mg/ml is
well adapted to the measurement. In terms of volume, users should
refer to the manufacturer’s specification/manual as it varies from
instrument to instrument, ranging from only a few μl to a couple
of ml.
A simple protocol for a DLS measurement is given below:
1. Switch on the instrument and select the experimental
temperature.
2. Wait for temperature stabilization.
3. If needed, clean the reusable cuvettes or plates with detergent
(e.g., 2%(v/v) Hellmanex) and water, and dry it with ethanol.
4. Filter buffers.
5. Load buffer and samples into the wells or the cuvettes (see Note
16). Avoid the formation of bubbles.
PDZ Sample Quality Assessment
113
6. Setup a program containing 5–20 acquisitions with an acquisition time of 2–10 s per sample depending on the presence of
dust or particles that may disturb the measurement. Measurements should be done in triplicates using automatic laser power
and detector attenuation.
7. If the DLS instrument has a camera, check for potential bubble
or precipitate.
8. During data analysis, the buffer quality should be first checked.
9. Check the shape of the autocorrelation curve for the protein
(Fig. 1b, inset). Check the fit and the error of the measurement
and adjust the fitting limit accordingly. The presence of aggregates is indicated if particles with a Rh of 10–100 nm are
observed (Fig. 1b).
10. If the protein is not detectable due to the presence of aggregates or artifacts, the sample should be centrifuged using a
benchtop centrifuge (15 min, at full speed). However, it is
important to consider the potential loss of protein during
centrifugation; it is therefore recommended to measure the
concentrations before and after centrifugation.
3.3.2 Size Exclusion
Chromatography (SEC)
with Multiangle Light
Scattering (SEC-MALS)
Test runs with small injection volumes of the protein of interest
may be performed to choose a good column setup (see Note 17). In
line with the column, the refractometer and the light scattering
detectors allow the measurement of the molecular mass of all the
peaks of interest.
A simple protocol for a SEC-MALS experiment is given below:
1. Switch on the instrument.
4. Purge the system with water.
5. Connect the column to the SEC-SLS/SEC-MALS system.
6. Choose the flow speed and pressure limit according to the
column manufacturer’s instruction.
7. Equilibrate the system with at least 2 column volumes of water
(see Note 18).
8. Purge the system with the buffer of interest (see Note 19).
9. Equilibrate the system with at least 2 column volumes of buffer.
10. Purge the refractometer extensively until the baseline remains
stable for one column run (see Note 20).
11. Centrifuge the sample for 5 min in a benchtop centrifuge at full
speed.
12. Load the sample into the injection loop. The volume (usually
100 μl) that will be injected onto the column should be lower
than 1% of the total column volume to ensure a good separation. If the sample concentration needs to be quantified, flush
the injection loop to homogenize the sample injection volume.
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Célia Caillet-Saguy et al.
13. At the end of the run, analyze all peaks of the chromatogram
for sample recovery and molecular mass determination according to the manufacturer’s manual. If the column type and the
buffer conditions are chosen correctly to avoid nonspecific
binding of the PDZ, you should recover nearly 100% of your
sample. The baseline for the detectors should be set up wisely,
that is, in a region where no proteins elute, such as the region
prior to the void volume of the column (first third of the
column volume). The baseline should remain constant if the
sample is in the same buffer as the elution buffer and if the
temperature is strictly controlled.
3.4 Conformational
Stability/Folding State
3.4.1 Circular
Dichroism (CD)
CD spectroscopy is a fast and easy method that is particularly
suitable for quickly judging the folding state of a protein; to compare the structure of proteins obtained from different sources; to
analyze the impact of point mutations on the structure of a protein;
to judge the stability of the structure facing environmental changes
(pH, salinity); to determine the impact on the structure in the
presence of a ligand.
One important point to improve the quality of the result is to
use well adapted buffer; it must not have an absorbance higher than
1 in the range of the spectrum (see Note 21). CD spectrometers
typically measure from longer to shorter wavelengths. The combination of accumulations and the time over which the instrument
averages each data point needs to be optimized to get the best
signal to noise (S/N) ratio. The parameters of a measurement
should be carefully chosen [20]. Usually, the CD signal, that is,
the ellipticity θ, the high voltage, and the absorbance are recorded.
In order to obtain reliable, interpretable, and high-quality CD data,
the absorbance of the sample (including buffer) should be between
1 and 2, that is, on most laboratory instruments the high voltage
should be kept below 600 V. A CD spectrum of the buffer is
recorded with the same set of parameters that will be applied in
the sample measurement to allow a baseline subtraction. A CD
spectrum recorded in the 180–260 nm range allows to evaluate
the secondary structure content and then the folding state of the
protein. The resulting spectrum has a characteristic shape and
magnitude determined by the secondary structure elements. The
fraction of each secondary structure element (α-helices, β-sheets,
turns, and disordered structures) is evaluated using different algorithms combining a set of reference spectra [43]. Dichroic signals in
the near UV (250–330 nm, absorption zone of aromatic amino
acids) provide information on the environment of these aromatic
amino acids and are a signature of tertiary and quaternary structure.
In these wavelengths, the CD signals of proteins are weak; cuvettes
with larger pathlengths and higher concentrations of PDZ sample
are required.
PDZ Sample Quality Assessment
115
CD is particularly suited to the study of structural changes
induced by environmental variations (pH, temperature, detergents,
cosolvents, buffers, . . .). Among the advantages of CD compared to
other techniques, we can mention that one works in dilute solution,
with small amounts of product (a few tens micrograms), without
molecular size limit on the protein. The measurements are simple
and fast, allowing kinetic measurements at very short times. Moreover, the measurement is not destructive and it is possible to
recover the sample after the acquisition of the spectrum.
Figure 4 shows far-UV CD spectra of free MAST2-PDZ, free
PDZ-binding motif peptide and the complex MAST2-PDZ with a
PDZ-binding motif peptide in blue, orange, and gray, respectively.
The similarity of spectra of MAST2-PDZ free or complexed to a
PDZ ligand, in blue and gray respectively, is indicative of the
maintenance of the PDZ folding upon PDZ–ligand binding while
the peptide alone (in orange) is unfolded.
Through this technique, the thermal stability of a protein can
also be evaluated by looking at the CD shape of spectra recorded at
different temperatures; this operation can also be done continuously with a temperature spectrum at a fixed wavelength, at 222 nm
for example to follow the helical content of PDZ domains. Thermal
stability is now often evaluated by DSC or DSF (see the section
below).
Procedure for a standard CD acquisition;
1. Switch on the instrument and select the experimental
temperature.
2. Wait for temperature stabilization.
3. Clean the cell with water, dry with ethanol (with detergent,
e.g., 2%(v/v) Hellmanex, if needed).
4. Centrifuge the buffer using a benchtop centrifuge (15 min, at
full speed) to remove any aggregates.
5. Fill the cell with 40–50 μl of buffer. Avoid the formation of
bubbles.
6. Record ellipticity values every 0.5 nm, averaged over 2 s
between 195 and 260 nm. Three to five successive scans are
collected.
7. Clean again the cell with water, dry with ethanol.
8. Fill the cell with 40–50 μl of PDZ sample. Avoid the formation
of bubbles.
9. Collect the data with the same parameters as the ones used for
the buffer spectra.
10. The final spectrum of PDZ sample is obtained by averaging the
3–5 successive scans and subtracting the baseline spectrum of
the buffer.
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Célia Caillet-Saguy et al.
3.4.2 Nano Differential
Scanning Fluorimetry
(nanoDSF)
A simple protocol to run a nanoDSF experiment with a MAST2PDZ solution free or in complex with a peptide on the Prometheus
NT.48 is given below:
1. Turn on the instrument and allow the system to warm up for at
least 15 min.
2. Clean the system with 70% ethanol.
3. Prepare in four different PCR tubes: 10 μl of buffer, 10 μl of
PDZ sample, 10 μl of PDZ sample with peptide a tenfold
concentration excess and 10 μl of a tenfold more concentrated
peptide solution.
4. Insert a capillary into the tubes and position them in order on
the rack in the DSF instrument.
5. Adjust the excitation power so that the fluorescence signal is
greater than 2000 counts to obtain clear denaturation
transitions.
6. Set the temperature range from 20 to 95 C with a desired
temperature gradient of (between 0.1 and 5 C).
7. Perform all measurements with the same temperature ramp.
8. Start the measurement.
3.4.3 Differential
Scanning
Calorimetry (DSC)
DSC is useful to characterize the stability of a protein by measuring
the emission or absorption of heat from a biomolecule during a
controlled increase or decrease in temperature. This allows the
study of molecules in a native state and is widely applied in protein
engineering, for rational drug design and biopharmaceutical production, when the development of stable proteins is a critical goal.
The thermal core of a DSC system consists of two cells, a
reference and a sample cell maintained at the same temperature,
as they are heated. To perform a DSC measurement, the reference
cell is first filled with buffer and the sample cell with the sample
solution. These cells are then heated at a constant scan rate. The
absorption of heat that occurs when a protein unfolds causes a
temperature difference (ΔT) between the cells, resulting in a thermal gradient across the Peltier units. From the ΔT values, DSC
profiles are generated to provide information on thermal stability
by measuring the thermal transition temperature (thermal transition midpoint or melting temperature, Tm) and the energy required
to break the stabilization interactions of the tertiary structure
(enthalpy) of proteins. Comparisons are made between samples
(e.g., wild-type and mutant proteins or various conditions of
buffer) and differences in the derived values indicate differences in
thermal stability and structural conformation. Compared with
other methods of evaluating the thermal stability of protein conformations, DSC is cost-effective and only requires few sample
preparation steps.
PDZ Sample Quality Assessment
117
We previously performed DSC on the PDZ domain of the
tyrosine phosphatase PTPN3 (PTPN3-PDZ) [34] free or in complex with various PBMs known to interact with the PTPN3-PDZ. A
single endothermic peak was observed in the DSC thermograms of
PTPN3-PDZ free or complexed. The free domain showed a Tm of
41 C, while Tm values between 45 C and 52 C were observed for
PTPN3-PDZ complexed to different PBMs. Thus, an increase of
4–11 C in the Tm was observed depending on the sequence of the
ligands, showing that the PBM binding onto PTPN3-PDZ stabilizes the domain in all complexes (Fig. 5a). A gain in thermal
stability is also observed for other PDZ domains such as MAST2PDZ, whose Tm increases from 47 C to 55–62 C upon PBM
binding (Fig. 5b). The ligand binding increases the Tm of the PDZ
domain, either by stabilizing the native conformation or by the
destabilization of the unfolded state.
3.4.4 NMR
Compared to other spectroscopic techniques, NMR sensitivity level
is low. To record spectra with a sufficient signal to noise (S/N) ratio
within rational acquisition time the sample needs to be quite concentrated. Currently, the common samples correspond to a volume
of 500 μl or 200 μl (5 mm or 3 mm tubes) containing 50–1000 μM
of a medium-sized protein (10–20 kDa). The sample buffer also
contains 1–5% (v/v) D2O to record a reference signal (lock signal)
to compensate for small changes/drifts of the magnetic field. The
NMR tube is cleaned with a soft tissue from the outside before it is
inserted into the instrument to remove anything spilled on the tube
or grease from the fingertips. When the sample is inserted into the
spectrometer and the temperature is equilibrated, the lock signal is
defined. The inhomogeneities in the magnetic field are then corrected (shim), and the impedance of the probes for each nucleus is
precisely adjusted. This is called “tuning the probe”. Finally, the
length of the 90 proton pulse is determined. The setup of the
experiment can be automated and can be achieved within
15–20 min. The quality of an NMR spectrum depends on the
S/N ratio and the resolution that are first related to the strength
of the static magnetic field. The S/N ratio also depends on the
concentration of the sample and the molecular mass and intrinsic
dynamics of the protein, which affect the linewidth of peaks in the
spectrum. The S/N ratio correlates with the number of FIDs
accumulated and is enhanced by using a cryoprobe. The resolution
of an NMR spectrum depends on the linewidth and the separation
of the resonances, which is directly correlated with the magnetic
field strength. Furthermore, the separation of the signals is determined by the number of data points collected in the respective
dimension.
A one-dimensional (1D) 1H NMR spectrum of a protein
requires a less number of molecules and is fast (less than 1 min)
to perform and can unambiguously indicate if a protein is folded or
118
Célia Caillet-Saguy et al.
not. Protein folding is then observed with the dispersion of the
resonance peaks, reflecting the structure of the protein (large dispersion ¼ stable folding), on 1D proton spectra or on
two-dimensional (2D) 1H-15N correlation spectra if the protein is
15
N-labeled.
In the 1D 1H spectrum of a well-folded protein, the peaks are
narrow and sharp and distributed over a large range of chemical
shifts meaning a good signal dispersion; signals can be especially
found at 1H resonance values lower than 0.5 ppm or higher than
8.5 ppm corresponding to high field-shifted methyl group protons
and downfield-shifted amide protons respectively. In contrast, the
peaks are broader and not as widely dispersed in the spectrum of an
unfolded or partially folded protein [44].
For example, in a fast 1D NMR spectrum of MAST2-PDZ
(Fig. 6a), we observed sharp and narrow peaks that cover a large
range of chemical shifts, that is, peaks in the negative ppm range
between 0 and -1 ppm corresponding to upfield methyl protons
located in the hydrophobic core of the domain, and peaks out near
10 ppm corresponding to downfield backbone amide protons. This
is a good indication that MAST2-PDZ is folded. This is a rather fast
and qualitative technique to assess protein folding. Also, all the
protons of the sample are seen in the 1D spectrum. In the past
decades, many methods have been developed to reduce the water
signal around 4.7 ppm; however, signals from protons from the
buffer can drastically complicate the analysis of spectra. For example, the 1D NMR spectrum of MAST2-PDZ (Fig. 6a) displays
intense Tris peaks around 3 ppm, but the peaks of the protein are
still visible.
The 2D 1H-15N correlation HSQC spectrum [45] provides the
“fingerprint” of a protein as the dispersion of cross-peaks is unique
to each protein folding and sequence. On the contrary, unfolded
proteins have all similar easily recognizable restrained patterns with
a very limited dispersion of cross peaks. In addition, the peak width
is related to the molecular mass of the molecule. We except for
MAST2-PDZ quite narrow peaks consistent with the small size of
the domain (Fig. 6b). Unexpected large peaks can be observed for
autoassociated domains or/and for proteins in conformational
exchange. For example, in the 2D spectrum of the free MAST2PDZ, we do not observe the expected number of peaks and the
peaks are broad due to an autoassociation and an exchange in the
low-to-medium NMR timescales between monomers and dimers
while upon binding of the ligand many additional peaks (in red)
appear and peaks are sharper when MAST2-PDZ recovers its
monomeric form [8, 36]. In conclusion, NMR spectrum is highly
sensitive to the folded state, oligomerization and aggregation state
of a protein, and is thus efficient for the quality control of protein
production [46].
PDZ Sample Quality Assessment
119
A simple protocol to run an NMR experiment is given below:
1. Transfer each sample into an NMR tube, add 1–5% D2O and
insert the sample plus the spinner into the magnet with the
sampler changer.
2. Adjust manually the probe equipped with an ATM module for
each nucleus adapted to the experience to ensure the best
sensitivity of the probe.
3. Lock the magnet to keep its field constant and stable during the
data acquisition and shim the magnet to make it uniform and
homogenous for a sample to get a reasonable resolution and
line shape.
4. Determine the 90 transmitter pulse.
5. Setup Parameters and Acquire Data.
6. Process Data to convert data into NMR spectra.
7. Conformational stability/folding state.
4
Notes
1. Absorbance at the characteristic wavelength of 280 nm (i.e.,
A280 nm) cannot be used for quantitative analysis without
protein sequence since the amino acid composition varies
from one protein to another. Indeed, variations in amino acid
composition give molar absorptivities (ε280 nm) that vary
from one protein to another.
2. Add glycerol gives the solution better to put down samples in
the wells. If the solution is yellow while it should be blue
because BBP turns yellow at acidic pH, add a few microliters
of NaOH to turn blue the solution.
3. The materials are already fully described in [39].
4. The buffer should contain at least 50 mM of salt to avoid
interactions with the column matrix and 0.1% (w/v) NaN3 to
prevent microbial growth. All solutions must be freshly filtered
and degassed to avoid background scattering and an unstable
baseline.
5. Cuvettes with pathlengths ranging from 0.01 to 10 mm are
used with a 20-1000 μl volume of protein solution at a concentration of 50 μg/ml to 1 mg/ml.
6. If you load 10 μg that corresponds to 10 μl at 1 mg/ml of
protein, you will need sensitivity of about 100 ng per band to
detect contamination. The detection limit of Coomassie blue
staining is approximately 100 ng per band, whereas silver stains
and fluorescent dye have a detection limit of approximately
1 ng of protein per band [20].
120
Célia Caillet-Saguy et al.
7. Using gradient gels with increasing acrylamide concentration
from top to bottom usually 4–12% allows the separation and
visualization of large proteins (200 kDa) that can still enter the
gel at the top and small (10 kDa) polypeptides such as PDZ
domains on the same gel as they retain small and fast-moving
proteins in the bottom of the gel.
8. Proteins larger than 800 kDa will not enter in the
polyacrylamide gel.
9. Check that there is no air bubble in the cuvette.
10. Record a spectrum over a wide wavelength range with a wavelength as a starting point where the solute does not absorb.
11. HCCA, DHB, and sDHB for PDZ samples with molecular
masses below 20 kDa and sinapinic acid is usually used for
constructs encompassing PDZ domain above 20 kDa. However, sinapinic acid produces adducts that may impede the
resolution and the precision of the m/z. In this case, the
HCCA matrix could be a good alternative for large
PDZ-containing constructs.
If the buffer is supplemented with more than 1% glycerol
or detergent, the best practice is to concentrate the sample and
then dilute it in water to reach the concentration required, to
minimize the effect of the detergent or glycerol. Sometimes,
the concentration of the purified PDZ is below what is required
to perform the MALDI-TOF analysis. Concentration and
desalting of PDZ constructs of molecular masses lower than
15 kDa could be achieved on a ZipTip C18. ZipTip C4 is used
for PDZ constructs of higher molecular mass.
12. High laser power triggers the loss of resolution and precision
on the m/z.
13. A database of the most common protein post translation modifications is found here: https://abrf.org/delta-mass
14. Between 25 and 50 pmol of PDZ are required. In case of
quantity limitation, use a ZipTip to desalt and concentrate
the protein. Keep in mind that the PDZ solution must be
pure to avoid false positive.
15. To remove dust or salt crystals, the buffer must be filtered.
16. For quality control, we do not recommend to filter or centrifuge the sample in order to detect aggregates; if DLS is used for
other purposes, it is advisable to filter the sample and centrifuge it for 5 min in a benchtop centrifuge at full speed before
loading.
17. Care should be taken to choose the column as an elongated
10 kDa protein may behave on the column like a globular
35 kDa protein. Be aware that SEC columns are often
described by their separative power on globular protein.
PDZ Sample Quality Assessment
121
18. Columns are usually stored in 20% (v/v) ethanol. Therefore,
the column must first be washed extensively with water (at least
2 column volumes) as ethanol may precipitate salts from the
buffer.
19. The buffer should be stable over time as any change, such as
oxidation, will lead to a drift in the baseline of the refractometer. The software baseline correction can compensate negligible drift.
20. High concentrations of chloride ions absorb at wavelengths
lower than 215 nm and limit the use of the spectrum below
that wavelengths. To avoid scattering, buffer and sample
should be filtered and degassed.
21. Chloride ions absorb strongly in the far-UV so low concentrations of NaCl and KCl in CD buffer are recommended. The
best way would be to prepare a phosphate buffer by mixing diand monosodium phosphates. In addition, DTT, detergents,
glycerol, or imidazole that absorb in the far-UV should be
removed.
Acknowledgments
The authors acknowledge Florence Cordier for technical support in
NMR. The authors thank the molecular biophysics facility at Institut Pasteur for providing cutting-edge instruments.
References
1. Weatheritt RJ, Luck K, Petsalaki E, Davey NE,
Gibson TJ (2012) The identification of short
linear motif-mediated interfaces within the
human
interactome.
Bioinformatics
28:976–982. https://doi.org/10.1093/bioin
formatics/bts072
2. Caillet-Saguy
C,
Maisonneuve
P,
Delhommel F, Terrien E, Babault N,
Lafon M, Cordier F, Wolff N (2015) Strategies
to interfere with PDZ-mediated interactions in
neurons: what we can learn from the rabies
virus. Prog Biophys Mol Biol 119:53–59.
https://doi.org/10.1016/j.pbiomolbio.
2015.02.007
3. Luck K, Charbonnier S, Travé G (2012) The
emerging contribution of sequence context to
the specificity of protein interactions mediated
by PDZ domains. FEBS Lett 586:2648–2661.
https://doi.org/10.1016/j.febslet.2012.03.
056
4. Ye F, Zhang M (2013) Structures and target
recognition modes of PDZ domains: recurring
themes and emerging pictures. Biochem J
455:1–14.
https://doi.org/10.1042/
BJ20130783
5. Wang CK, Pan L, Chen J, Zhang M (2010)
Extensions of PDZ domains as important
structural and functional elements. Protein
Cell 1:737–751. https://doi.org/10.1007/
s13238-010-0099-6
6. Delhommel F, Cordier F, Bardiaux B,
Bouvier G, Colcombet-Cazenave B, Brier S,
Raynal B, Nouaille S, Bahloul A, ChamotRooke J, Nilges M, Petit C, Wolff N (2017)
Structural characterization of whirlin reveals an
unexpected and dynamic supramodule conformation of its PDZ tandem. Structure
25:1645–1656.e5. https://doi.org/10.1016/
j.str.2017.08.013
7. Long J, Wei Z, Feng W, Yu C, Zhao Y, Zhang
M (2008) Supramodular nature of GRIP1
revealed by the structure of its PDZ12 tandem
in complex with the carboxyl tail of Fras1. J
Mol Biol 375:1457–1468. https://doi.org/
10.1016/j.jmb.2007.11.088
122
Célia Caillet-Saguy et al.
8. Delhommel F, Chaffotte A, Terrien E,
Raynal B, Buc H, Delepierre M, Cordier F,
Wolff N (2015) Deciphering the unconventional peptide binding to the PDZ domain of
MAST2. Biochem J 469:159–168. https://
doi.org/10.1042/BJ20141198
9. Wu J, Yang Y, Zhang J, Ji P, Du W, Jiang P,
Xie D, Huang H, Wu M, Zhang G, Wu J, Shi Y
(2007) Domain-swapped dimerization of the
second PDZ domain of ZO2 may provide a
structural basis for the polymerization of claudins. J Biol Chem 282:35988–35999. https://
doi.org/10.1074/jbc.M703826200
10. Chang BH, Gujral TS, Karp ES, BuKhalid R,
Grantcharova VP, MacBeath G (2011) A systematic family-wide investigation reveals that
30% of mammalian PDZ domains engage in
PDZ-PDZ
interactions.
Chem
Biol
18:1143–1152. https://doi.org/10.1016/j.
chembiol.2011.06.013
11. Laemmli UK (1970) Cleavage of structural
proteins during the assembly of the head of
bacteriophage T4. Nature 227:680–685.
https://doi.org/10.1038/227680a0
12. Gasteiger E, Hoogland C, Gattiker A,
Duvaud S, Wilkins M, Appel RD, Bairoch AM
(2005) Protein identification and analysis tools
on the ExPASy server. In: Walker JM (ed) The
proteomics protocols handbook. Humana
Press, pp 571–607
13. Noble JE (2014) Quantification of protein
concentration using UV absorbance and Coomassie dyes. Methods Enzymol 536:17–26.
https://doi.org/10.1016/B978-0-12420070-8.00002-7
14. Glasel JA (1995) Validity of nucleic acid purities monitored by 260nm/280nm absorbance
ratios. BioTechniques 18:62–63
15. Pace CN, Vajdos F, Fee L, Grimsley G, Gray T
(1995) How to measure and predict the molar
absorption coefficient of a protein. Protein Sci
4:2411–2423. https://doi.org/10.1002/pro.
5560041120
16. Karas M, Hillenkamp F (1988) Laser desorption ionization of proteins with molecular
masses exceeding 10,000 daltons. Anal Chem
60:2299–2301.
https://doi.org/10.1021/
ac00171a028
17. Suckau
D,
Resemann
A
(2003)
T3-sequencing: targeted characterization of
the N- and C-termini of undigested proteins
by
mass
spectrometry.
Anal
Chem
75:5817–5824.
https://doi.org/10.1021/
ac034362b
18. Philo JS (2006) Is any measurement method
optimal for all aggregate sizes and types? AAPS
J 8:E564–E571. https://doi.org/10.1208/
aapsj080365
19. Nobbmann U, Connah M, Fish B, Varley P,
Gee C, Mulot S, Chen J, Zhou L, Lu Y, Shen F,
Yi J, Harding SE (2007) Dynamic light scattering as a relative tool for assessing the molecular
integrity and stability of monoclonal antibodies. Biotechnol Genet Eng Rev 24:117–128
20. Raynal B, Lenormand P, Baron B, Hoos S,
England P (2010) Quality assessment and optimization of purified protein samples: why and
how? Microb Cell Factories 13. https://doi.
org/10.1186/s12934-014-0180-6
21. Fekete S, Beck A, Veuthey J-L, Guillarme D
(2014) Theory and practice of size exclusion
chromatography for the analysis of protein
aggregates.
J
Pharm
Biomed
Anal
101:161–173.
https://doi.org/10.1016/j.
jpba.2014.04.011
22. Sahin E, Roberts CJ (2012) Size-exclusion
chromatography with multi-angle light scattering for elucidating protein aggregation
mechanisms.
Methods
Mol
Biol
899:403–423.
https://doi.org/10.1007/
978-1-61779-921-1_25
23. Daviter T, Chmel N, Rodger A (2013) Circular
and linear dichroism spectroscopy for the study
of protein-ligand interactions. Methods Mol
Biol 1008:211–241
24. Schuck P (2003) On the analysis of protein
self-association by sedimentation velocity analytical ultracentrifugation. Anal Biochem
320:104–124.
https://doi.org/10.1016/
S0003-2697(03)00289-6
25. Demeler B (2010) Methods for the design and
analysis of sedimentation velocity and sedimentation equilibrium experiments with proteins.
Curr Protoc Protein Sci:1–24. https://doi.
org/10.1002/0471140864.ps0713s60
26. Balbo A, Zhao H, Brown PH, Schuck P (2009)
Assembly, loading, and alignment of an analytical ultracentrifuge sample cell. J Vis Exp:
e1530. https://doi.org/10.3791/1530
27. Schuck P (2000) Size-distribution analysis of
macromolecules by sedimentation velocity
ultracentrifugation and lamm equation modeling. Biophys J 78:1606–1619. https://doi.
org/10.1016/S0006-3495(00)76713-0
28. Micsonai A, Wien F, Bulyáki É, Kun J, Moussong É, Lee Y-H, Goto Y, Réfrégiers M, Kardos J (2018) BeStSel: a web server for accurate
protein secondary structure prediction and fold
recognition from the circular dichroism spectra. Nucleic Acids Res 46:W315–W322.
https://doi.org/10.1093/nar/gky497
29. Medrano G, Dolan MC, Condori J, Radin DN,
Cramer CL (2012) Quality assessment of
recombinant proteins produced in plants.
Methods Mol Biol 824:535–564. https://doi.
org/10.1007/978-1-61779-433-9_29
PDZ Sample Quality Assessment
30. Structural Genomics Consortium, China
Structural Genomics Consortium, Northeast
Structural Genomics Consortium, Gr€aslund S,
Nordlund P, Weigelt J, Hallberg BM, Bray J,
Gileadi O, Knapp S, Oppermann U,
Arrowsmith
C,
Hui
R,
Ming
J,
Dhe-Paganon S, Park H, Savchenko A, Yee A,
Edwards A, Vincentelli R, Cambillau C, Kim R,
Kim S-H, Rao Z, Shi Y, Terwilliger TC, Kim
C-Y, Hung L-W, Waldo GS, Peleg Y, Albeck S,
Unger T, Dym O, Prilusky J, Sussman JL, Stevens RC, Lesley SA, Wilson IA, Joachimiak A,
Collart F, Dementieva I, Donnelly MI, Eschenfeldt WH, Kim Y, Stols L, Wu R, Zhou M,
Burley SK, Emtage JS, Sauder JM,
Thompson D, Bain K, Luz J, Gheyi T,
Zhang F, Atwell S, Almo SC, Bonanno JB,
Fiser A, Swaminathan S, Studier FW, Chance
MR, Sali A, Acton TB, Xiao R, Zhao L, Ma LC,
Hunt JF, Tong L, Cunningham K, Inouye M,
Anderson S, Janjua H, Shastry R, Ho CK,
Wang D, Wang H, Jiang M, Montelione GT,
Stuart DI, Owens RJ, Daenke S, Schütz A,
Heinemann U, Yokoyama S, Büssow K, Gunsalus KC (2008) Protein production and purification. Nat Methods 5:135–146. https://doi.
org/10.1038/nmeth.f.202
31. Dupeux F, Röwer M, Seroul G, Blot D, Márquez JA (2011) A thermal stability assay can
help to estimate the crystallization likelihood of
biological samples. Acta Crystallogr D Biol
Crystallogr 67:915–919. https://doi.org/10.
1107/S0907444911036225
32. Bruce D, Cardew E, Freitag-Pohl S, Pohl E
(2019) How to stabilize protein: stability
screens for thermal shift assays and nano differential scanning fluorimetry in the virus-X project. J Vis Exp. https://doi.org/10.3791/
58666
33. Boivin S, Kozak S, Meijers R (2013) Optimization of protein purification and characterization using Thermofluor screens. Protein Expr
Purif 91:192–206. https://doi.org/10.1016/
j.pep.2013.08.002
34. Genera M, Samson D, Raynal B, Haouz A,
Baron B, Simenel C, Guerois R, Wolff N,
Caillet-Saguy C (2019) Structural and functional characterization of the PDZ domain of
the human phosphatase PTPN3 and its interaction with the human papillomavirus E6
oncoprotein. Sci Rep 9:7438. https://doi.
org/10.1038/s41598-019-43932-x
35. Monsellier E, Bedouelle H (2005) Quantitative measurement of protein stability from
unfolding equilibria monitored with the fluorescence maximum wavelength. Protein Eng
Des Sel 18:445–456. https://doi.org/10.
1093/protein/gzi046
123
36. Terrien E, Chaffotte A, Lafage M, Khan Z,
Préhaud C, Cordier F, Simenel C,
Delepierre M, Buc H, Lafon M, Wolff N
(2012) Interference with the PTEN-MAST2
interaction by a viral protein leads to cellular
relocalization of PTEN. Sci Signal 5:ra58.
https://doi.org/10.1126/scisignal.2002941
37. Rath A, Glibowicka M, Nadeau VG, Chen G,
Deber CM (2009) Detergent binding explains
anomalous SDS-PAGE migration of membrane proteins. Proc Natl Acad Sci U S A
106:1760–1765. https://doi.org/10.1073/
pnas.0813167106
38. Shirai A, Matsuyama A, Yashiroda Y,
Hashimoto A, Kawamura Y, Arai R,
Komatsu Y, Horinouchi S, Yoshida M (2008)
Global analysis of gel mobility of proteins and
its use in target identification. J Biol Chem
283:10745–10752.
https://doi.org/10.
1074/jbc.M709211200
39. Duhoo Y, Girault V, Turchetto J, Ramond L,
Durbesson F, Fourquet P, Nominé Y,
Cardoso V, Sequeira AF, Brás JLA, Fontes
CMGA, Travé G, Wolff N, Vincentelli R
(2019) High-throughput production of a new
library of human single and tandem PDZ
domains allows quantitative PDZ-peptide
interaction
screening
through
highthroughput holdup assay. Methods Mol Biol
2025:439–476.
https://doi.org/10.1007/
978-1-4939-9624-7_21
40. Ivarsson Y, Wawrzyniak AM, Kashyap R,
Polanowska J, Betzi S, Lembo F,
Vermeiren E, Chiheb D, Lenfant N,
Morelli X, Borg J-P, Reboul J, Zimmermann
P (2013) Prevalence, specificity and determinants of lipid-interacting PDZ domains from
an in-cell screen and in vitro binding experiments. PLoS One 8:e54581. https://doi.org/
10.1371/journal.pone.0054581
41. Wawrzyniak AM, Kashyap R, Zimmermann P
(2013) Phosphoinositides and PDZ domain
scaffolds. Adv Exp Med Biol 991:41–57.
https://doi.org/10.1007/978-94-007-63319_4
42. Layne E (1957) Spectrophotometric and turbidimetric methods for measuring proteins.
Methods Enzymol 3:447–454
43. Lees JG, Miles AJ, Wien F, Wallace BA (2006)
A reference database for circular dichroism
spectroscopy covering fold and secondary
structure
space.
Bioinformatics
22:1955–1962. https://doi.org/10.1093/bio
informatics/btl327
44. Page R, Peti W, Wilson IA, Stevens RC,
Wüthrich K (2005) NMR screening and crystal
quality of bacterially expressed prokaryotic and
124
Célia Caillet-Saguy et al.
eukaryotic proteins in a structural genomics
pipeline. Proc Natl Acad Sci U S A
102:1901–1905. https://doi.org/10.1073/
pnas.0408490102
45. Bodenhausen G, Ruben DJ (1980) Natural
abundance nitrogen-15 NMR by enhanced
heteronuclear spectroscopy. Chem Phys Lett
69:185–189.
https://doi.org/10.1016/
0009-2614(80)80041-8
46. Kwan AH, Mobli M, Gooley PR, King GF,
Mackay JP (2011) Macromolecular NMR
spectroscopy for the non-spectroscopist.
FEBS J 278:687–703. https://doi.org/10.
1111/j.1742-4658.2011.08004.x
Chapter 7
Crystallographic Studies of PDZ Domain–Peptide
Interactions of the Scribble Polarity Module
Janesha C. Maddumage, Bryce Z. Stewart, Patrick O. Humbert,
and Marc Kvansakul
Abstract
The determination of high-resolution crystal structures of cell polarity regulatory proteins bound to their
functional interactors has proven to be invaluable for deciphering the underlying molecular mechanisms.
Here we describe methods to identify suitable complexes of cell polarity protein domains bound to
interacting ligands with subsequent preparation of such complexes for X-ray crystallographic analysis.
Key words Epithelial cell polarity, Scribble module, Structural biology, X-ray crystallography
1
Introduction
The asymmetric distribution of macromolecules into different
compartments within a cell is a phenomenon known as cell polarity,
and is essential for correct execution of a wide range of biological
processes in metazoans. There are four major types of polarity
including apical–basal polarity, planar cell polarity, front–rear polarity, and asymmetrical cell division [1]. Irrespective of their differences, one common feature central to these types of polarity is the
coordinated involvement of a highly conserved set of proteins
known as polarity regulators. Loss-of-function mutations in these
polarity regulators cause polarity loss contributing to the development of disease such as cancer [2].
The Scribble module is one of the key protein regulators
involved in the establishment and maintenance of apical–basal
polarity. It consists of three members, Scribble, Dlg, and Lgl,
with these proteins controlling epithelial polarity in conjunction
with two other key modules the Par and the Crumbs complexes
Janesha C. Maddumage and Bryce Z. Stewart contributed equally to this work.
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021
125
126
Janesha C. Maddumage et al.
[1, 2]. Being scaffolding proteins, Scribble, Dlg, and Lgl interact
with many other proteins using their specific domains, not only to
impart their functions in cell polarity but also to regulate cell
signalling, all leading to effects on cell proliferation, differentiation
and cellular migration [3].
Scribble belongs to the LAP (LRR and PDZ) protein family,
and consists of 16 Leucine-rich repeats (LRRs), two LAP specific
domains (LAPSADa and LAPSADb) and four PDZ (post-synaptic
density protein 95 (PSD 95), Discs large homologue (Dlg1) and
Zonula Occludens-1 (ZO1) domains [3]. Dlg belongs to the
membrane-associated guanylate kinase homolog (MAGUK) protein family, comprises a Src homology 3 (SH3) domain, a Hook
domain, three PDZ domains, and a guanylate kinase-like (GUK)
domain [3]. The majority of interactions of Scribble and Dlg are
mediated by their PDZ binding domains [3]. PDZ domains usually
comprise 80–100 amino acids forming two α-helices wrapping
around six β-strands with a single binding site between α-helix
2 and β-strand 2 [4, 5]. PDZ domains are associated with varying
biological processes such as target signaling, membrane protein
localization, cytoskeletal regulation, and cell polarity processes
[5]. They are highly conserved in structure and function throughout evolution and notably abundant in vertebrates, with over
250 PDZ domains in humans alone [4, 6, 7].
The combined use of phage display, protein microarray, yeast
two-hybrid, proteomics and structural approaches have delivered
significant insight into the mode of interaction of PDZ domains
with their interactors [5, 8–10]. PDZ domains typically target short
carboxyl terminal amino acid sequences of 7–10 residues in length
[11], where the last residue of the target C-terminal sequence is
designated as residue 0 and the preceding residues are counted
backward with the overall pattern being . . .(-3)-(-2)-(-1)-(0)COOH
. Three classes of PDZ domain binding motifs (PBMs) can be
distinguished: the first class consists of (X)-[T/S]-(X)-(ϕ)COOH
consensus sequence where X is any residue, and ϕ is any hydrophobic residue. The second PBM class comprises (X)-(ϕ)-(X)-(ϕ)COOH
and PBM class 3 (X)-[E/D])-(X)-(ϕ)COOH . PDZ domains can
target PBMs with varying selectivity and specificity depending on
the type of class a PBM contains [4, 5, 11]. In addition to
C-terminal PBMs, internal binding sites have also been identified
[12] including within the internal protein sequence of cell polarity
interactors [13, 14]. Additional modifiers of interactions include
post translational modifications such as phosphorylation, which
have been shown to be potent modifiers of binding affinities to
PDZ domains and can either decrease or enhance affinity [15–
17]. Dysregulation of PDZ domain mediated interactions by cell
polarity regulatory proteins is implicated in various disease such as
neurological and psychiatric disorders, viral infection, and cancer
[3, 18, 19]. Consequently an increased understanding of the
Biophysics of Scribble Module PDZ Interactions
127
selectivity and specificity of PDZ domain protein–protein interactions will be instrumental in providing novel insights into disease
mechanisms.
Human Scribble and Dlg PDZ domains have been shown to
interact with a wide range of proteins involved in various cellular
functions such as signaling, trafficking, and polarity, many of which
are implicated in disease progression [3]. A number of structures of
Dlg and Scribble PDZ-PBM protein complexes have been solved
using either X-ray crystallography or NMR and deposited in the
protein data bank (PDB) as listed in Table 1. However, for the
Scribble module, the vast majority of its interactions and their
molecular basis remain to be characterized in humans and other
relevant model organisms. Here we will focus on methods and
strategies related to PDZ-related structure determination of Scribble module components using X-ray crystallography. Furthermore,
we discuss some of the biophysical analyses such as circular dichroism spectroscopy (CD) and isothermal titration calorimetry (ITC)
which complement efforts to determine X-ray crystallographic
structures.
Circular Dichroism (CD) is a spectroscopic method that utilizes the differential absorption of right-and-left polarized light
through an optically active molecule [20]. This is an invaluable
tool for the determination of secondary structure of proteins,
with CD spectra in the far UV region between 180 nm and
260 nm revealing different secondary structure features of sample
proteins: alpha helix, parallel and antiparallel beta sheet, turn, and
other. Thus, CD spectroscopy can determine the percentage of
secondary structure components a protein contains. CD spectroscopy can also determine the folding state of expressed and purified
recombinant proteins, and reveal how mutations affect protein
structure and stability. Variations of the secondary structure of
proteins upon ligand bindings and protein–protein interactions in
different conditions such as temperature, pH, and salts, can also be
determined using CD spectroscopy [21]. While CD spectroscopy is
not a substitute for high-resolution structure determination by
X-ray crystallography, NMR, or cryo-EM, CD analysis significantly
improves the sample preparation for the higher resolution
techniques.
2
Materials
2.1 Sample
Preparation for CD
Analysis
1. Recombinant cell polarity protein (e.g., Scribble PDZ and Dlg
PDZ domains) purified to homogeneity.
2. PDZ storage buffer: 25 mM HEPES–NaOH pH 7.2,
150 mM NaCl.
3. Phosphate buffer: 50 mM sodium phosphate pH 7.0 (see Note 1).
4. Centrifugal concentrator (MWCO 3 kDa).
128
Janesha C. Maddumage et al.
Table 1
PDB entries of Scribble module PDZ–PBM complexes
Protein complex
Species
PDB
code
Scrib PDZ1: Guk-Holder
Drosophila
5WOU X-ray
[29]
SCRIB PDZ1: pMCC
Human
6MTU X-ray
[26]
SCRIB PDZ1: MCC
Human
6MTV
X-ray
[26]
SCRIB PDZ3: Beta-PIX
Human
5VWI
X-ray
[25]
SCRIB PDZ1: Beta-PIX
Human
5VWK
X-ray
[25]
SCRIB PDZ1: APC
Human
6MS1
X-ray
[30]
SCRIB PDZ1: SGEF
Human
6MYE
X-ray
[13]
SCRIB PDZ34 and target peptide
Human
4WYU X-ray
[28]
DLG1 PDZ1: APC
Human
3RL7
X-ray
[31]
DLG1 PDZ2: APC
Human
4G69
X-ray
[32]
DLG1 PDZ2: APC
Human
3RL8
X-ray
[31]
DLG1/SAP97 PDZ2:HPV51 E6
Human papillomavirus type 2M3M NMR
51/human
[33]
DLG1/SAP97 PDZ2:HPV-18 E6
Human
2OQS
NMR
[34]
Dlg1/Sap97 PDZ2:HPV18 E6
Human papillomavirus type 2IOL
18/rat
X-ray
[35]
Dlg1/Sap97 PDZ2:5HT2A
Mouse/rat
4OAJ
X-ray
Unpublished
Dlg1/Sap97 PDZ3:HPV18 E6
Human papillomavirus type 2IOI
18/rat
X-ray
[35]
Dlg3/SAP102 PDZ3:Stargazin
derivative peptides
Rat/mouse
3JXT
X-ray
[36]
Dlg4/PSD-95 PDZ1:5HT2C
Rat
2MHO X-ray
Unpublished
Dlg4/PSD-95 PDZ12: cypin
Rat
2KA9
NMR
[37]
DLG4/PSD-95 PDZ3: SynGAP
Human, mouse
5JXB
X-ray
[38]
Dlg4/PSD-95 PDZ3: CRIPT
Rat
5HEB
X-ray
[39]
Dlg4/PSD-95 PDZ3: CRIPT(T-2F)
Rat
5HED X-ray
[39]
Dlg4/PSD-95 PDZ3(G330T): CRIPT Rat
5HEY
X-ray
[39]
Dlg4/PSD-95 PDZ3(G330T): CRIPT Rat
(T-2F)
5HF1
X-ray
[39]
Dlg4/PSD-95 PDZ3(H372A): CRIPT Rat
5HFB
X-ray
[39]
Dlg4/PSD-95 PDZ3(H372A): CRIPT Rat
(T-2F)
5HFC
X-ray
[39]
Method Reference
(continued)
Biophysics of Scribble Module PDZ Interactions
129
Table 1
(continued)
Protein complex
Species
PDB
code
Method Reference
Dlg4/PSD-95 PDZ3(G330T,
H372A): CRIPT
Rat
5HFE
X-ray
Unpublished
Dlg4/PSD-95 PDZ3(G330T,
H372A): CRIPT (T-2F)
Rat
5HFF
X-ray
[39]
Dlg4/PSD-95 PDZ3: CFMOCKKETEV
Rat
5D13
X-ray
Unpublished
2.2 Sample
Preparation
for Isothermal Titration
Calorimetry (ITC)
1. Recombinant PDZ domain protein purified to homogeneity in
storage buffer at a concentration of 75 μM.
2. Synthetic C-terminal PBM peptide of eight residues in length
(see Note 2) such as the Superpeptide (RSWFETWV) dissolved
in storage buffer at a final concentration of 900 μM.
3. MicroCalTM iTC200 System (GE Healthcare) or equivalent.
4. Washing solution: 10% SDS in dH2O.
5. MicroCal Origin® version 7.0 (or newer) software (OriginLab™ Corporation).
2.3 Sample
Preparation for X-Ray
Crystallography
1. Recombinant cell polarity protein (e.g., Scribble PDZ and Dlg
PDZ domains) purified to homogeneity in PDZ storage buffer.
2. Synthetic peptide encoding a C-terminal PDZ binding motif
(PBM) dissolved in dH2O (e.g., MCC, β-PIX, APC) (see Note
3).
3. 1 mL centrifugal concentrator (MWCO 3 kDa, Millipore).
3
Methods
3.1 Sample
Preparation for CD
Spectroscopy
When preparing samples for CD, an important factor to be considered is the choice of the buffer which the protein is prepared
in. Most buffers absorb light in the far UV (below 250 nm)
range, thereby reducing the amount of light available for the actual
chromophore (protein) to absorb. Many recombinantly expressed
cell polarity proteins are stored in the HEPES buffer (25 mM
HEPES pH 7.2, 150 mM NaCl) or Tris buffer (25 mM Tris
pH 8, 150 mM NaCl) for their long-term storage. Unfortunately,
these buffers absorb in the far UV range (see Table 2) [22]. Therefore, to minimise the buffer effects as much as possible, it is important to use low concentrations of salts and buffers that are not
strong UV absorbers (see Table 2) [22]. Since chloride ions strongly
130
Janesha C. Maddumage et al.
Table 2
Absorbance of Various Salt and Buffer Substances in the Far-UV Region
Compound
pH
No absorbance
above (nm)
Tricine
pH 8.5
Tris
Absorbance of a 10 mM solution
in a 1.0 mm cuvette at:
210 nm
200 nm
190 nm
180 nm
230
0.22
0.44
>0.5
>0.5
pH 8.0
220
0.02
0.13
0.24
>0.5
HEPES
pH 7.5
230
0.37
0.5
>0.5
>0.5
PIPES
pH 7.0
230
0.2
0.49
0.29
>0.5
MOPS
pH 7.0
230
0.1
0.34
0.28
>0.5
MES
pH 6.0
230
0.07
0.29
0.29
0.15
Cacodylate
pH 6.0
210
0.01
0.20
0.22
NaClO4
170
0
0
0
0
NaF, KF
170
0
0
0
0
Boric Acid
180
0
0
0
0
NaCl
205
0
0.02
>0.5
>0.5
Na2HPO4
210
0
0.05
0.3
>0.5
NaH2PO4
195
0
0
0.01
0.15
Na Acetate
220
0.03
0.17
>0.5
>0.5
Glycine
220
0.03
0.1
>0.5
>0.5
Diethylamine
240
0.4
>0.5
>0.5
>0.5
NaOH
pH 12
230
>0.5
>2
>2
>2
Boric Acid, NaOH
pH 9.1
200
0
0
0.09
0.3
absorb in the far UV range, samples for CD should not contain
NaCl and should be non-HCl buffered. Other buffer components
such as DTT, imidazole, DMSO, and glycerol should also be
removed via dialysis or buffer exchange before proceeding with
the experiment.
The protocol below describes the sample preparation of cell
polarity proteins for CD spectroscopy. It can be used to estimate
the secondary structure components and their proper folding patterns, before obtaining higher-resolution structural information by
X-ray crystallography.
1. Wash 15 mL centrifugal concentrator with 15 mL of phosphate
buffer by centrifugation.
2. Add 0.1 mg of cell polarity protein with the final concentration
of 0.3 mg/mL to the concentrator.
Biophysics of Scribble Module PDZ Interactions
131
3. Top up the concentrator with phosphate buffer and centrifuge
for 20 min at 4000 g.
4. After the first centrifugation, discard the flow through and top
up the concentrator again with fresh phosphate buffer and spin
again for up to 20 min at 4000 g.
5. Repeat step 4 for another 4 times.
6. Concentrate the protein sample (now in phosphate buffer) to a
final concentration of 0.3 mg/mL and use approximately
300 μL for CD experiment (see Note 1).
3.2 Interactions
of Polarity Protein PDZ
Domains with Ligands
Techniques used to analyze protein interactions such as surface
plasmon resonance (SPR) can be hampered by the requirement
for immobilization of proteins before the interaction can be
observed. Isothermal titration calorimetry (ITC) provides a quantitative in-solution method for measuring protein interactions
without immobilization. In a single measurement, ITC allows for
accurate measurements of the binding constant (KD), reaction
stoichiometry (n), enthalpy (ΔH), and entropy (ΔS) of a given
biomolecular binding event whereby heat is either released or
absorbed [23]. ITC is highly sensitive to small differences in concentration of buffer components and pH, thus making control
measurements to establish buffer effects advisable. In addition to
buffer control measurements, control measurements to gauge PDZ
domain protein quality interactions are useful. For this purpose, we
use a synthetic peptide that binds to a vast range of PDZ domains
with high affinity termed superpeptide (RSWFETWV), which was
identified via phage display experiments [24].
1. Ensure reference cell contains water.
2. Rinse sample cell and syringe with dH2O and storage buffer
three times each (see Note 4).
3. Titrations are performed at 25 C with a stirring speed of
750 rpm.
4. A total of 20 injections with 2 μL of the peptide solution each
and a spacing of 180 s were titrated into the 200 μL protein
sample, except for the first injection which was only 0.4 μL (see
Note 5).
5. Process the raw thermograms with Origin® to obtain the binding parameters of each interaction. Typical examples are shown
in Fig. 1.
3.3 Preparation
of Cell-Polarity
Protein–Peptide
Complexes
for Crystallization
Scribble and Dlg PDZ domains interact with a diverse range of
known partners, and preparation of complexes of these domains
with the interacting partners is a key step to understand their multifunctionality and promiscuous behavior, and ultimately their role in
disease mechanisms. In this example we demonstrate how to
132
Janesha C. Maddumage et al.
Fig. 1 Biophysical characterization of Scribble PDZ domain–ligand complexes. (a) Cartoon structure of D.
melanogaster Scribble PDZ1 domain bound to Gukh with associated interaction measured by ITC and CD
spectrum of Scribble PDZ1 domain. (b) Cartoon structure of human Scribble PDZ1 domain bound to phosphoMCC with associated interaction measured by ITC and CD spectrum of Scribble PDZ1 domain. (c) Cartoon
structure of human Scribble PDZ3 domain bound to β-Pix with associated interaction measured by ITC and CD
spectrum of Scribble PDZ3 domain
prepare a complex of an individual PDZ domain with a peptide
corresponding to the C-terminal PDZ binding motif (PBM) of the
interacting partner that has been identified using ITC. In general
PDZ domains typically harbor micromolar affinities toward their
binding partners, and the outlined method has been successfully
used to prepare complexes for crystallization trials of Scribble/Dlg
PDZ domains with peptides [25, 26] and led to the determination
of a number of crystal structures (see Table 1 and Fig. 1).
Biophysics of Scribble Module PDZ Interactions
133
1. Wash 1 mL centrifuge concentrator with PDZ storage buffer.
2. Add 1 mg of PDZ domain in final sample buffer.
3. Slowly add at least 2–5 molar excess of the target peptide to
centrifugal concentrator while stirring with pipette to avoid
local precipitation of sample (see Note 6).
4. Concentrate the complex to a final concentration of 5 mg/mL
(see Note 7).
3.4 Crystallization
of Tandem Scribble
PDZ Domains Bound
to Interacting Peptides
4
As Scribble and Dlg consist of multi-PDZ domains, two or more of
these PDZ domains can be connected to each other in tandem to
form PDZ supramodules, which may give rise to unique binding
properties with their interactors that are different from when they
exist as individual domains [27]. Since these tandem PDZ domains
are often connected to each other via very short linkers, these
domains act as a one structural and functional unit [27]. Preparation of complexes of tandem domains with a target peptide/peptide can be also achieved using a similar method as mentioned
above. The protein concentration and the protein–peptide ratio
may vary. A successful crystallization example is the structure determination of Scribble PDZ34 (8 mg/mL) in complex with a commercially synthesized peptide (S-W-F-Q-T-D-L) in a 5:1 peptide–
protein molar ratio (PDB Id:4WYU) [28].
Notes
1. Phosphate buffer is prepared by mixing Na2HPO4 and
NaH2PO4 to achieve the desired pH in order to avoid the use
of HCl.
2. Suitable peptide length for ITC is eight or more amino acids.
However, peptides of four amino acids in length have been
successfully characterized.
3. The synthetic peptide stocks are stored in dH2O at a concentration of 5 mM.
4. It is crucial to have an identical pH for sample and peptide
buffers when performing ITC to prevent spurious heat peaks
due to the heat of dilution of protons.
5. Usually PDZ domain proteins yield high-quality thermograms
at concentrations of 75 μM for protein and 900 μM for peptide;
however, some concentration optimization may be required.
6. Peptide concentration in crystallization trials should be at least
at a twofold molar excess.
7. Final concentration of the sample for crystallization experiment
may vary with each sample and should be confirmed using a
spectrophotometer to measure UV absorbance at 280 nm
wavelength.
134
Janesha C. Maddumage et al.
References
1. Nelson WJ (2003) Adaptation of core mechanisms to generate cell polarity. Nature
422:766–774
2. Halaoui R, McCaffrey L (2015) Rewiring cell
polarity signaling in cancer. Oncogene
34:939–950
3. Stephens R, Lim K, Portela M, Kvansakul M,
Humbert PO, Richardson HE (2018) The
scribble cell polarity module in the regulation
of cell signaling in tissue development and
tumorigenesis. J Mol Biol 430:3585–3612
4. Subbaiah VK, Kranjec C, Thomas M, Banks L
(2011) PDZ domains: the building blocks regulating
tumorigenesis.
Biochem
J
439:195–205
5. Ye F, Zhang M (2013) Structures and target
recognition modes of PDZ domains: recurring
themes and emerging pictures. Biochem J
455:1–14
6. Alié A, Manuel M (2010) The backbone of the
post-synaptic density originated in a unicellular
ancestor of choanoflagellates and metazoans.
BMC Evol Biol 10:34
7. Belahbib H, Renard E, Santini S, Jourda C,
Claverie JM, Borchiellini C, Le Bivic A
(2018) New genomic data and analyses challenge the traditional vision of animal epithelium evolution. BMC Genomics 19:393
8. Daulat AM, Puvirajesinghe TM, Camoin L,
Borg JP (2018) Mapping cellular polarity networks using mass spectrometry-based strategies. J Mol Biol 430:3545–3564
9. Pires HR, Boxem M (2018) Mapping the
polarity
interactome.
J
Mol
Biol
430:3521–3544
10. Wen W, Zhang M (2018) Protein complex
assemblies in epithelial cell polarity and asymmetric cell division. J Mol Biol 430:3504–3520
11. Songyang Z, Fanning AS, Fu C, Xu J, Marfatia
SM, Chishti AH, Crompton A, Chan AC,
Anderson JM, Cantley LC (1997) Recognition
of unique carboxyl-terminal motifs by distinct
PDZ domains. Science 275:73–77
12. Hillier BJ, Christopherson KS, Prehoda KE,
Bredt DS, Lim WA (1999) Unexpected
modes of PDZ domain scaffolding revealed by
structure of nNOS-syntrophin complex. Science 284:812–815
13. Awadia S, Huq F, Arnold TR, Goicoechea SM,
Sun YJ, Hou T, Kreider-Letterman G,
Massimi P, Banks L, Fuentes EJ, Miller AL,
Garcia-Mata R (2019) SGEF forms a complex
with scribble and Dlg1 and regulates epithelial
junctions and contractility. J Cell Biol
218:2699–2525
14. Penkert RR, DiVittorio HM, Prehoda KE
(2004) Internal recognition through PDZ
domain plasticity in the Par-6-Pals1 complex.
Nat Struct Mol Biol 11:1122–1127
15. Chetkovich DM, Chen L, Stocker TJ, Nicoll
RA, Bredt DS (2002) Phosphorylation of the
postsynaptic
density-95
(PSD-95)/discs
large/zona occludens-1 binding site of stargazin regulates binding to PSD-95 and synaptic
targeting of AMPA receptors. J Neurosci
22:5791–5796
16. Clairfeuille T, Mas C, Chan AS, Yang Z, TelloLafoz M, Chandra M, Widagdo J, Kerr MC,
Paul B, Mérida I, Teasdale RD, Pavlos NJ,
Anggono V, Collins BM (2016) A molecular
code for endosomal recycling of phosphorylated cargos by the SNX27-retromer complex.
Nat Struct Mol Biol 23:921–932
17. Sundell
GN,
Arnold
R,
Ali
M,
Naksukpaiboon P, Orts J, Güntert P, Chi CN,
Ivarsson Y (2018) Proteome-wide analysis of
phospho-regulated PDZ domain interactions.
Mol Syst Biol 14:e8129
18. Grant SGN (2019) Synapse diversity and
synaptome architecture in human genetic disorders. Hum Mol Genet 21:219–225
19. Thomas M, Banks L (2018) Upsetting the balance: when viruses manipulate cell polarity control. J Mol Biol 430:3481–3503
20. Franck B (1965) Optical circular dichroism.
Principles, measurements, and applications.
Von L. Velluz, M. Legrand und M. Grosjean.
Angew Chem 77:875–875
21. Greenfield NJ (2006) Using circular dichroism
spectra to estimate protein secondary structure.
Nat Protoc 1:2876–2890
22. Aviv Biomedical, Inc. Circular dichroism spectrophotometer. Instrument manual, 2009
23. Saponaro A (2018) Isothermal titration calorimetry: a biophysical method to characterize
the interaction between label-free biomolecules in solution. Bio-protocol 8:e2957
24. Zhang Y, Yeh S, Appleton BA, Held HA, Kausalya PJ, Phua DC, Wong WL, Lasky LA,
Wiesmann C, Hunziker W, Sidhu SS (2006)
Convergent and divergent ligand specificity
among PDZ domains of the LAP and zonula
occludens (ZO) families. J Biol Chem
281:22299–22311
25. Lim KYB, Gödde NJ, Humbert PO, Kvansakul
M (2017) Structural basis for the differential
interaction of scribble PDZ domains with the
guanine nucleotide exchange factor beta-PIX. J
Biol Chem 292:20425–20436
Biophysics of Scribble Module PDZ Interactions
26. Caria S, Stewart BZ, Jin R, Smith BJ, Humbert
PO, Kvansakul M (2019) Structural analysis of
phosphorylation-associated interactions of
human MCC with scribble PDZ domains.
FEBS J 286:4910–4925
27. Feng W, Zhang M (2009) Organization and
dynamics of PDZ-domain-related supramodules in the postsynaptic density. Nat Rev Neurosci 10:87–99
28. Ren J, Feng L, Bai Y, Pei H, Yuan Z, Feng W
(2015) Interdomain interface-mediated target
recognition by the scribble PDZ34 supramodule. Biochem J 468:133–144
29. Caria S, Magtoto CM, Samiei T, Portela M,
Lim KYB, How JY, Stewart BZ, Humbert
PO, Richardson HE, Kvansakul M (2018)
Drosophila melanogaster Guk-holder interacts
with the scribbled PDZ1 domain and regulates
epithelial development with scribbled and discs
large. J Biol Chem 293(12):4519–4531
30. How JY, Caria S, Humbert PO, Kvansakul M
(2019) Crystal structure of the human scribble
PDZ1 domain bound to the PDZ-binding
motif of APC. FEBS Lett 593:533–542
31. Zhang Z, Li H, Chen L, Lu X, Zhang J, Xu P,
Lin K, Wu G (2011) Molecular basis for the
recognition of adenomatous polyposis coli by
the discs large 1 protein. PLoS One 6:e23507
32. Slep KC (2012) Structure of the human discs
large 1 PDZ2- adenomatous polyposis coli
cytoskeletal polarity complex: insight into peptide engagement and PDZ clustering. PLoS
One 7:e50097
33. Mischo A, Ohlenschl€ager O, Hortschansky P,
Ramachandran R, Görlach M (2013)
135
Structural insights into a wildtype domain of
the oncoprotein E6 and its interaction with a
PDZ domain. PLoS One 8:e62584
34. Liu Y, Henry GD, Hegde RS, Baleja JD (2007)
Solution structure of the hDlg/SAP97 PDZ2
domain and its mechanism of interaction with
HPV-18 papillomavirus E6 protein. Biochemistry 46:10864–10874
35. Zhang Y, Dasgupta J, Ma RZ, Banks L,
Thomas M, Chen XS (2007) Structures of a
human papillomavirus (HPV) E6 polypeptide
bound to MAGUK proteins: mechanisms of
targeting tumor suppressors by a high-risk
HPV oncoprotein. J Virol 81:3618–3626
36. Sainlos M, Iskenderian-Epps WS, Olivier NB,
Choquet D, Imperiali B (2013) Caged monoand divalent ligands for light-assisted disruption of PDZ domain-mediated interactions. J
Am Chem Soc 135:4580–4583
37. Wang W, Weng J, Zhang X, Liu M, Zhang M
(2009) Creating conformational entropy by
increasing interdomain mobility in ligand binding regulation: a revisit to N-terminal tandem
PDZ domains of PSD-95. J Am Chem Soc
131:787–796
38. Zeng M, Shang Y, Araki Y, Guo T, Huganir
RL, Zhang M (2016) Phase transition in postsynaptic densities underlies formation of synaptic complexes and synaptic plasticity. Cell
166:1163–1175
39. Raman AS, White KI, Ranganathan R (2016)
Origins of allostery and evolvability in proteins:
a case study. Cell 166:468–480
Chapter 8
A Fluorescence-Based Assay to Determine PDZ–Ligand
Binding Thermodynamics
Young Joo Sun and Ernesto J. Fuentes
Abstract
Postsynaptic density-95, disks-large, and zonula occludens-1 (PDZ) domain interactions with cognate
linear binding motifs (i.e., PDZ-binding motifs or PBMs) are important for many biological processes and
can be pathological when disrupted. There are hundreds of PDZ–PBM interactions reported but few have
been quantitatively determined. Moreover, PDZ–PBM interactions have been identified as potential
therapeutic targets. To thoroughly understand PDZ–PBM binding energetics and their specificity, we
have developed a sensitive and quantitative equilibrium binding assay. Here, we describe a protocol for
determining PDZ–PBM binding energetics using fluorescence anisotropy-based methodology.
Key words PDZ domain, PDZ-binding motif, Fluorescence anisotropy, Protein–protein binding,
CASK, Scribble, SGEF
1
Introduction
Postsynaptic density-95, Disks-large, and Zonula occludens-1
(PDZ) proteins are ubiquitously found in many types of mammalian cells and regulate the spatial and temporal function of a diverse
set of signaling pathways. A distinguishing feature of these proteins
is the small (~90 amino acids, ~10 kDa), structurally conserved
protein–protein interaction module known as a PDZ domain that
selectively interacts with linear C-terminal and internal peptide
motifs (i.e., PDZ-binding-motifs or PBMs) [1]. PDZ–PBM interactions and their specificity are critical for many biological processes
including the maintenance of cell polarity, neuronal development,
and signal transduction. Thus, it is not surprising that genetic
mutations in PDZ proteins or perturbation of PDZ–PBM interactions can contribute to pathologies such as neuronal disorders and
complications form brain injury, cancer, cystic fibrosis, and viral
infections (reviewed in [1]).
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021
137
138
Young Joo Sun and Ernesto J. Fuentes
Although PDZ–PBM interactions have been extensively characterized, there remains inadequate understanding of the general
molecular mechanisms that determine PDZ–PBM specificity, particularly for internal PBMs. This is the result of the low sequence
identity among PDZ domain homologs, promiscuous binding
profiles, and context-dependent interaction mechanisms. Physiological PDZ–PBM interactions have relatively weak binding affinities, with a dissociation constant (Kd) ranging from μM to low
mM [2–4]. To thoroughly characterize PDZ–PBM interactions it is
necessary to determine the binding energetics (i.e., ΔGb, Gibbs free
energy of binding) of PDZ–PBM interactions. The binding energetics coupled with high-resolution structural information and
mutagenesis can provide deep insights into the binding mechanism
and specificity of PDZ–PBM interactions [5–14]. Importantly, this
information can be used to design potential PDZ–PBM protein–
ligand inhibitors. Indeed, over the past ~10 years PDZ–PBM interactions have been identified as potential therapeutic targets
(reviewed in [1]). Here, we describe a general protocol for determining the binding energetics of PDZ–PBM interactions using a
robust and simple fluorescence anisotropy-based assay sensitive to
interactions with dissociation constants in the 1 to ~500 μM range
[15–17].
2
Materials
2.1
Equipment
1. A spectrofluorometer equipped with excitation and emission
polarizers and a magnetic stirrer is used to collect fluorescence
anisotropy data [15]. Here, we use a Fluorolog-3 (Jobin Yvon,
Horiba, NJ) controlled by the FluorEssence V3.8 software
program (Jobin Yvon, Horiba, NJ). The spectrofluorometer is
set to an excitation wavelength at 340 nm and an emission
wavelength at 550 nm, specific for the dansyl [5-(dimethyl
amino)naphthalene-1-sulfonyl] chloride fluorophore (see
Note 1), with constant stirring at 25 C. The instrument
light slit widths are adjusted in the range of 3–9 nm to optimize
the signal-to-noise ratio and maximum output intensity—aiming for ~one million counts per second on the detector (see
Note 2).
2. A quartz cuvette containing 4 polished windows, compatible
with a magnetic stirring platform is used. We use a
2 mL,10 mm length path cuvette equipped with a stopper
and stir bar (Hellma, NY; catalog #119F-10-40).
2.2 Constructs,
Medium, and Reagents
for PDZ Domain
Purification
1. PDZ domains cloned into bacterial expression plasmids are
used. Here we use the CASK PDZ domain cloned into
pET28a (Novagen) and the Scribble PDZ1 cloned into a modified pET21a (Novagen) [18].
PDZ Domain Binding Thermodynamics
139
2. Luria–Bertani (LB) medium: 10 g/L tryptone, 5 g/L yeast
extract, and 10 g/L sodium chloride.
3. 100 mg/mL ampicillin (pET21a) or 50 mg/mL kanamycin
(pET28a) stock solution.
4. E. coli bacterial strain BL21(DE3) (Novagen).
5. 1 M isopropyl 1-thio-β-d-galactopyranoside (IPTG) stock
solution.
2.3 Reagents
and Solutions
All buffers should be of the highest purity available.
1. Binding buffer: 20 mM sodium phosphate pH 6.8, 50 mM
sodium chloride, and 0.5 mM ethylenediaminetetraacetic acid
(EDTA).
2. Peptides containing C-terminal PBMs are typically commercially synthesized. The peptides used here were chemically
synthesized by GenScript Inc. (Piscataway, NJ) and used at
>95% purity. Peptides corresponding to the C-terminus of
partner proteins were 8 amino acids long, N-terminally dansylated and contained a free carboxylate at the C-terminus. An
internal peptide from SH3-containing guanine nucleotide
exchange factor (SGEF) contained 14 amino acids and was
N-terminally dansylated. In addition, the C-terminal carboxylate group was amidated (see Note 3).
3. The amino acid sequence of the C-termini of the following
human proteins was used in the development of this protocol:
Neurexin-1 (residues 1470-1477: NKDKEYYV), Caspr4 (residues 1301-1308: ENQKEYFF), and Syndecan-1 (residues
303–310: TKQEEFYA). The internal SGEF peptide was
derived from residues 42–55: KPNGLLITDFPVED [18].
4. Resuspend ~2 mg aliquot of lyophilized peptide in 1 mL of
binding buffer and adjust the pH to 6.8 to obtain a highly
concentrated (1–2 mM) master stock solution.
5. Prepare a working stock solution of 0.130 mM stock
dansylated-PBM by diluting the master stock with binding
buffer (see Notes 4 and 5).
6. Store all PBM peptide stock solutions at 20 C in the dark (see
Note 6).
3
Methods
3.1 Purified CASK
and Scribble PDZ
Domains
A protocol for the expression of CASK and Scribble PDZ domains
is described below. Purification of these recombinant PDZ domains
is beyond the scope of this chapter but can be achieved using similar
methods and buffers as previously published [7, 8, 11]. In brief,
CASK PDZ was purified using cation exchange and size-exclusion
140
Young Joo Sun and Ernesto J. Fuentes
chromatography (Superdex 75, GE Healthcare Life Sciences) while
purification of the first PDZ domain of Scribble was carried out
using nickel-chelate affinity chromatography. The N-terminal
6 His affinity tag of Scribble PDZ1 was removed by proteolysis
with recombinant tobacco etch virus (rTEV) protease for 36 h at
4 C. Undigested protein, cleaved 6 His tag, and His-tagged
rTEV were separated from the Scribble PDZ1 domain by nickelchelate chromatography. The digested PDZ1 protein was further
purified using Superdex 75 size-exclusion chromatography. All
proteins were concentrated to ~0.5–2.0 mM in binding buffer
(see Note 5). The prepared samples were generally used immediately (see Note 7).
1. Supplement LB medium with the appropriate antibiotic for the
desired PDZ domain (final concentration of 100 μg/mL ampicillin for the mpET21a CASK PDZ and 50 μg/mL kanamycin
for pET28a Scribble PDZ1).
2. Grow bacterial cells transformed with either CASK or Scribble
PDZ constructs in LB medium supplemented with antibiotic at
37 C under vigorous agitation to an optical density of a
0.6–0.8 measured at 600 nm wavelength.
3. Cool cultures to 18 C.
4. Induce protein expression by adding IPTG to 1 mM final
concentration.
5. Incubate for an additional 16–18 hrs at 18 C.
6. Harvest bacteria by centrifugation.
7. Proceed with the purification of proteins and concentrate to
~0.5 to 2.0 mM in binding buffer (see Note 5). The prepared
samples are generally used immediately (see Note 7).
3.2 Experimental
Sample Preparation
The experimental design calls for using serial dilutions of the PDZ
domain protein to cover the concentration range of 1–400 μM in
discrete steps. Thus, a 0.5 mM stock provides reliable quantification ranging from 1 to 100 μM Kd, while a 2.0 mM stock provides
reliable quantification ranging from 20 to 200 μM Kd. The PDZ
domain concentration range and number of titration points can be
adjusted in subsequent experiments to optimize the titration and
obtain a more reliable Kd determination.
3.2.1 Cuvette and PBM
Peptide Preparation
1. Rinse the cuvette with distilled and deionized H2O (ddH2O)
and ethanol using a vacuum cuvette washer (see Note 8).
2. Air dry the cuvette after rinsing and/or cleaning.
3. Add stir bar and 1290 μL of binding buffer.
4. Add 10 μL of dansylated-PBM peptide stock solution to the
cuvette to obtain a 1.0 μM PBM peptide concentration (see
Note 2).
PDZ Domain Binding Thermodynamics
141
5. Gently mix the sample using a pipette being careful to avoid
introducing bubbles (see Note 9).
6. Cover the cuvette to prevent introducing dust particles and to
minimize the dansyl-fluorophore light exposure (see Note 10).
3.2.2 Preparation of PDZ
Domain Dilution Stock
Solutions
All the PDZ domain diluted solutions described below should be
prepared in advance and on ice.
1. Prepare 26 μL of a 100-fold diluted PDZ domain stock per
experiment (concentration 5–20 μM).
2. Prepare 10 μL of a tenfold diluted PDZ domain stock per
experiment (concentration 50–200 μM).
3. Prepare 656 μL PDZ domain stock per experiment
(concentration 0.5–2 mM).
3.3 Binding Assay
Parameters and Data
Collection
Below are the experimental parameters defined in the FluorEssence
V3.8 software. Anisotropy data is collected after each addition of
PDZ protein.
1. Experiment type: anisotropy.
2. Temperature: 25 C (or other desired temperature).
3. Number of data points is ~29 (see Note 11).
4. After adding the appropriate volume of PDZ domain, gently
mix the sample using a micropipette to avoid out-gassing followed by the measurement of fluorescence anisotropy (three
measurements are taken and averaged). A typical titration
experiment uses the following PDZ domain concentrations
and volumes per titration step.
5. Data point 1: the initial background measurement without any
PDZ domain. This serves as the baseline control.
6. Data point 2 and 3: add 3 μL of 100-fold diluted stock PDZ
domain (concentration 5–20 μM) at each step.
7. Data point 4 and 5: add 5 μL of 100-fold diluted stock PDZ
domain (concentration 520 μM) at each step.
8. Data point 6: add 10 μL of 100-fold diluted stock PDZ domain
(concentration 5–20 μM).
9. Data point 7 and 8: add 5 μL of tenfold diluted stock PDZ
domain (concentration 50–200 μM) at each step.
10. Data point 9 and 10: add 3 μL of PDZ domain stock (concentration 0.5–2 mM) at each step.
11. Data point 11 and 12: add 5 μL of PDZ domain stock (concentration 0.5–2 mM) at each step.
12. Data point 13 and 14: add 10 μL of PDZ domain stock
(concentration 0.5–2 mM) at each step.
142
Young Joo Sun and Ernesto J. Fuentes
PDZ
Vstock
( L)
0
3
3
5
5
10
5
5
3
3
5
5
10
10
20
20
20
20
20
20
20
60
60
60
60
60
60
60
60
60
added
Anisotropy
Trials
StdErrAniso (%)
0.0283667495155055
0.0274686828061678
0.029768683937325
0.0286828152753334
0.0289592691855773
0.0279688320612641
0.0302485839847933
0.0282586258847039
0.0288619028287986
0.0306373436606708
0.0338288561137255
0.0348562383472131
0.0373794594208286
0.0405176201406837
0.0473504993123733
0.0502904025786218
0.0522171120090482
0.0532442012233099
0.0540631222118147
0.057123332166628
0.0589739543476869
0.0641761287443581
0.0657427875852492
0.0689649065572496
0.0693739146349071
0.0709325840527179
0.0716128324888053
0.0738748579212796
0.0735578220264764
0.0757814104680961
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4.6773
3.5528
1.898
5.5035
1.7806
4.9895
3.0321
1.3758
1.7269
3.6168
3.1001
1.9562
2.08
1.0867
2.422
1.5704
1.3038
1.3866
3.3913
4.6715
2.042
5.0507
4.1818
3.3367
2.9321
1.7149
0.6701
4.1681
2.4364
1.4858
Fig. 1 Sample data output of a PDZ–ligand PBM binding assay. The “PDZ
added
Vstock” column shows the volume of stock solution for each data point of
the titration. The “Anisotropy” column shows the average anisotropy value
measured for each titration data point. The “Trials” column shows the number
of measurements used for calculating the average anisotropy. The “StdErrAniso”
column shows the standard deviation of the anisotropy
13. Data point 15 to 21: add 20 μL of PDZ domain stock (concentration 0.5–2 mM) at each step.
14. Data point 22 to 29: add 60 μL of PDZ domain stock (concentration 0.5–2 mM) at each step.
15. Each PDZ–PBM binding assay is collected in triplicate (using
either biological or technical replicates). Figure 1 shows an
example of the output from a typical titration dataset.
3.4
Data Processing
Data processing requires the calculation of PDZ concentration for
each titration step, [PDZ]n, where n is the individual data point
collected.
1. The PDZ concentration of the first data point is 0: [PDZ]1 ¼ 0.
PDZ Domain Binding Thermodynamics
PDZ
CASK PDZ
PBM
SDC1
143
Dataset #
2
Protein Concentration (µM)
1229
Stock Conc. (µM)
0.00
12.29
12.29
12.29
12.29
12.29
122.9
122.9
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
1229
Added Vol. (µL)
0
3
3
5
5
10
5
5
3
3
5
5
10
10
20
20
20
20
20
20
20
60
60
60
60
60
60
60
60
60
Total Vol. (µL)
1300
1303
1306
1311
1316
1326
1331
1336
1339
1342
1347
1352
1362
1372
1392
1412
1432
1452
1472
1492
1512
1572
1632
1692
1752
1812
1872
1932
1992
2052
Conc. (µM)
0.00
0.03
0.06
0.10
0.15
0.24
0.70
1.16
3.91
6.65
11.19
15.69
24.60
33.38
50.55
67.25
83.47
99.25
114.60
129.54
144.08
185.49
223.86
259.50
292.70
323.70
352.72
379.93
405.51
429.59
Anisotropy
0.02798
0.03079
0.02941
0.03041
0.02941
0.03037
0.02993
0.02853
0.02696
0.03148
0.03043
0.03221
0.03707
0.03854
0.04198
0.04385
0.04766
0.05292
0.05184
0.05391
0.05279
0.05895
0.05972
0.06396
0.06525
0.06689
0.06948
0.07127
0.07036
0.07239
Base line corr.
0.00000
0.00281
0.00143
0.00243
0.00143
0.00239
0.00195
0.00055
-0.00102
0.00350
0.00245
0.00423
0.00909
0.01056
0.01400
0.01587
0.01969
0.02494
0.02386
0.02593
0.02481
0.03097
0.03174
0.03598
0.03727
0.03891
0.04150
0.04329
0.04238
0.04441
Fig. 2 Processed data of the CASK PDZ–SDC1 PBM binding assay. The concentration of CASK PDZ domain is
indicated. The column labeled “Stock Conc.” shows the concentration of the PDZ stock used for the collection
of each data point. The column labeled “Conc.” indicates the concentration of PDZ domain sample in the
cuvette for each data point. The “Base line corr.” column is the anisotropy value after baseline correction
(corrA)
2. The equation for calculating the PDZ concentration at [PDZ]n
data point is.
½PDZn ¼ ½PDZn1 tot V n1 þ ½PDZstock added V stock =tot V n
ð1Þ
where totVn is the total volume added over n titration steps,
tot
Vn-1 is total volume of PDZ protein added at the n-1 titration
point, and addedVstock is the volume added of stock PDZ
144
Young Joo Sun and Ernesto J. Fuentes
domain ([PDZ]stock) (i.e., totVn ¼ totVn-1 + addedVstock summed
over n titration steps). This calculation can be conveniently
performed in spreadsheet software (e.g., Microsoft Excel).
3. Baseline correction: The anisotropy value of the first data point
(A1 at [PDZ]1 ¼ 0) is subtracted from the anisotropy value of
each subsequent data point (An at [PDZ]n) to obtain the
corrected anisotropy at each PDZ concentration (corrAn ¼ An
- A1). Figure 2 shows an example of processed data computed
in a spreadsheet program.
3.5 Data Analysis
and Binding Curve
Presentation
1. The binding curves are fit to a standard hyperbolic binding
model:
B max ½PDZ
corr
A¼
ð2Þ
K d þ ½PDZ
where corrA is the corrected anisotropy at each titration step,
Bmax is the maximum anisotropy at PDZ domain saturation, Kd
is the dissociation constant, and [PDZ] is the total concentration of the PDZ domain in solution. SigmaPlot (Systat Software Inc., CA) was used to determine Bmax and Kd by fitting
the binding data to Eq. 2 using nonlinear regression analysis
(see Note 12) [6, 15]. The Kd of each PDZ–PBM pair is
measured in triplicate and reported as the mean and standard
error of the mean.
2. Each data point is normalized to the fitted Bmax for graphical
presentation of multiple binding curves in a single plot. Figure 3 shows the presentation of binding curves for CASK
PDZ– and Scribble PDZ1–ligand binding reactions.
3. The Gibbs free energy of binding (ΔGb) is calculated by
ΔG b ¼ RT∗ lnðKd Þ
ð3Þ
where R is the universal gas constant and T is the given experimental temperature. The error in free energy can be obtained
by propagation of the error in Kd.
4
Notes
1. The dansyl fluorophore can affect binding affinity by directly
interacting with PDZ domains [7, 8]. For relative affinity measurements this may not be an issue. PDZ domain–fluorophore
interactions can be minimized by using other fluorophores or
longer peptides [15].
2. Both the peptide concentration and slit-width can be adjusted
to optimize signal-to-noise ratio. However, the concentration
PDZ Domain Binding Thermodynamics
145
A
SGEF
NRXN1
SCD1
Caspr4
PDZ [ M]
B
PDZ/ligand
Kd ( m)
∆Gb (kcal/mol)
Scribble PDZ1/SGEF
7.3 ± 1.5
-7.01 ± 0.12
CASK PDZ/NRXN1
32.2 ± 4.6
-6.13 ± 0.08
CASK PDZ/SCD1
121 ± 19
-5.34 ± 0.10
CASK PDZ/Caspr4
N. B.
N. B.
Fig. 3 PDZ–PBM binding curves and energetics. (a) Representative binding curves for PDZ–PBM interactions.
The Caspr4 binding curve is an example of negative control C-terminal peptide that does not bind (i.e., N.B., no
binding) the CASK PDZ domain. The CASK PDZ domain binds C-terminal NRXN1 and SDC1 peptides. The
Scribble PDZ1 domain binds an internal peptide derived from SGEF. (b) Dissociation constants and Gibbs free
energy of binding for several PDZ–PBM interactions. The reported dissociation constants are the average and
standard error derived from at least three independent experiments
of peptide should be kept ~tenfold lower than the Kd to obtain
reliable fits of the data.
3. The peptide ligand can be either a C-terminal or internal ligand
derived from the target full-length protein. For C-terminal
ligands, generally 6–8 terminal residues are used but additional
residues may be involved, and this should be determined
empirically. Internal PBM sequences can also be used
[16]. Again, the exact residues will vary for each interaction
and should be determined empirically. Internal ligands also
146
Young Joo Sun and Ernesto J. Fuentes
should have their N-terminus acetylated to mimic an internal
protein sequence by avoiding an electrostatic dipole contribution (see [15] for additional details). In addition, the
C-terminus should be amidated to neutralize the free carboxylate group.
4. Peptides are typically purified via high-performance liquid
chromatography purification using acidic buffers (e.g., trifluoracetic acid) prior to lyophilization. Thus, care should be taken
to adjust the pH of the buffer solution upon solubilizing the
peptide. We use a benchtop pH meter equipped with microelectrode for small volume samples.
5. The concentration of peptide and protein in solution can be
measured by UV absorbance at 280 nm wavelength using a
spectrophotometer. The extinction coefficient can be calculated from the amino acid sequence (e.g., ExPASy—ProtParam) [19, 20]. A fluorophore can contribute to the
280 nm wavelength absorbance. However, the excitation and
emission wavelengths of the dansyl fluorophore are more than
10 nm away from 280 nm (λex ¼ 340, λem ¼ 550); thus, the
fluorophore should not significantly affect the peptide concentration determination at the 280 nm wavelength [15]. If the
peptide lacks amino acids with chromophores, one can use the
extinction coefficient of dansyl chloride (ε ~4350 M1 l cm1)
[21] to estimate the peptide concentration. Alternatively, one
can use a color-based protein assay (e.g., bicinchoninic acid or
Bradford assay, Thermo Scientific).
6. Dansylated peptide solutions should be kept away from light by
either covering them with aluminum foil or using tinted (light
free) microfuge tubes. Stock dansylated peptides are generally
stored in small aliquots to avoid repetitive freeze and thaw
cycles.
7. Using the PDZ protein sample immediately after purification is
highly recommended. Long-term storage of proteins at 4 C or
at 20 C can significantly affect protein stability. If storage is
required, the integrity of the PDZ sample should be tested
periodically with a known reference peptide.
8. The Hellmanex™ III (Hellma, NY) cleaning concentrate can
be used periodically to remove biological material from the
surface of cuvettes followed by thorough rinsing with ddH2O
and ethanol using a vacuum cuvette washer.
9. When preparing buffers for sample preparation, filtering and
degassing are highly recommended. Particulate matter and air
bubbles scatter light and disrupt the spectroscopic measurements. We filter buffers with a 0.45 μm membrane using a
vacuum filter unit attached to a dry vacuum system (Welch)
followed by continuous stirring under vacuum for 15–30 min.
PDZ Domain Binding Thermodynamics
147
10. To prevent fluorophore bleaching by light exposure, it is highly
recommended to cover the cuvette (containing the
fluorophore-peptide) in the spectrofluorometer with the lid
closed. The sample is now ready for the data acquisition. If
necessary, the sample can be equilibrated for several minutes
(typically 1–5 min) or until the measured anisotropy value is
stable over time.
11. The number of data points can vary depending on the sample
and affinity of the PDZ–ligand interaction. Data collection is
complete when the anisotropy value plateaus (i.e., three consecutive data points have similar anisotropy values). However,
we typically collect two or three additional data points after
approaching the anisotropy plateau to ensure binding saturation has been reached.
12. The following assumptions are made when fitting the binding
data. First, the PDZ–PBM binding stoichiometry is 1:1, which
is true for all known PDZ–PBM interactions. Second, the
concentration of free PDZ domain is on the order of the
total PDZ domain concentration. Third, there is no significant
change in fluorescence intensity (<10%) upon PDZ binding.
We have not observed a significant change in fluorescence
intensity in our experiments. Otherwise, correction factors
should be applied [15].
Acknowledgments
The authors thank members of the Fuentes laboratory for helpful
comments. E.J.F. is supported by National Institutes of Health
grant R21-AI135305.
References
1. Liu X, Fuentes EJ (2019) Emerging themes in
PDZ domain signaling: structure, function,
and inhibition. Int Rev Cell Mol Biol
343:129–218
2. Stiffler MA, Chen JR, Grantcharova VP, Lei Y,
Fuchs D, Allen JE et al (2007) PDZ domain
binding selectivity is optimized across the
mouse proteome. Science 317:364–369
3. Chen JR, Chang BH, Allen JE, Stiffler MA,
MacBeath G (2008) Predicting PDZ domainpeptide interactions from primary sequences.
Nat Biotechnol 26:1041–1045
4. Tonikian R, Zhang Y, Sazinsky SL, Currell B,
Yeh JH, Reva B et al (2008) A specificity map
for the PDZ domain family. PLoS Biol 6:e239
5. Shepherd TR, Klaus SM, Liu X, Ramaswamy S,
DeMali KA, Fuentes EJ (2010) The Tiam1
PDZ domain couples to Syndecan1 and promotes cell-matrix adhesion. J Mol Biol
398:730–746
6. Shepherd TR, Hard RL, Murray AM, Pei D,
Fuentes EJ (2011) Distinct ligand specificity of
the Tiam1 and Tiam2 PDZ domains. Biochemistry 50:1296–1308
7. Liu X, Shepherd TR, Murray AM, Xu Z,
Fuentes EJ (2013) The structure of the
Tiam1 PDZ domain/phospho-syndecan1
complex reveals a ligand conformation that
modulates protein dynamics. Structure
21:342–354
8. Liu X, Speckhard DC, Shepherd TR, Sun YJ,
Hengel SR, Yu L et al (2016) Distinct roles for
conformational dynamics in protein-ligand
interactions. Structure 24:2053–2066
148
Young Joo Sun and Ernesto J. Fuentes
9. Panel N, Sun YJ, Fuentes EJ, Simonson T
(2017) A simple PB/LIE free energy function
accurately predicts the peptide binding specificity of the Tiam1 PDZ domain. Front Mol
Biosci 4:65
10. Panel N, Villa F, Fuentes EJ, Simonson T
(2018) Accurate PDZ/peptide binding specificity with additive and polarizable free energy
simulations. Biophys J 114:1091–1102
11. Liu X, Golden LC, Lopez JA, Shepherd TR,
Yu L, Fuentes EJ (2019) Conformational
dynamics and cooperativity drive the specificty
of a protein-ligand interaction. Biophys J 116
(12):2314–2330
12. Halabi N, Rivoire O, Leibler S, Ranganathan R
(2009) Protein sectors: evolutionary units of
three-dimensional
structure.
Cell
138:774–786
13. McLaughlin RN Jr, Poelwijk FJ, Raman A,
Gosal WS, Ranganathan R (2012) The spatial
architecture of protein function and adaptation. Nature 491:138–142
14. Salinas VH, Ranganathan R (2018)
Coevolution-based inference of amino acid
interactions underlying protein function. elife
7:e34300
15. Shepherd TR, Fuentes EJ (2011) Structural
and thermodynamic analysis of PDZ-ligand
interactions. Methods Enzymol 488:81–100
16. Harris BZ, Hillier BJ, Lim WA (2001) Energetic determinants of internal motif recognition by PDZ domains. Biochemistry
40:5921–5930
17. Harris BZ, Lau FW, Fujii N, Guy RK, Lim WA
(2003) Role of electrostatic interactions in
PDZ domain ligand recognition. Biochemistry
42:2797–2805
18. Awadia S, Huq F, Arnold TR, Goicoechea SM,
Sun YJ, Hou T et al (2019) SGEF forms a
complex with scribble and Dlg1 and regulates
epithelial junctions and contractility. J Cell Biol
218:2699–2725
19. Pace CN, Vajdos F, Fee L, Grimsley G, Gray T
(1995) How to measure and predict the molar
absorption coefficient of a protein. Protein Sci
4:2411–2423
20. Grimsley GR, Pace CN (2004) Spectrophotometric determination of protein concentration.
Curr Protoc Protein Sci Chapter 3:Unit 3.1
21. Chen RF (1968) Dansyl labeled proteins:
determination of extinction coefficient and
number of bound residues with radioactive
dansyl chloride. Anal Biochem 25:412–416
Chapter 9
Unveiling the Folding Mechanism of PDZ Domains
Candice Gautier and Stefano Gianni
Abstract
Understanding the mechanism of folding of single domain proteins demands a complete characterization of
their equilibrium and kinetic properties. By using a well-studied class of protein domain, the PDZ domain,
here we exemplify the typical procedure to address this problem.
Key words Protein Stability, Equilibrium, Kinetics, Fluorescence, Protein Engineering
1
Introduction
Understanding the mechanism of folding of single domain proteins
is a difficult task that demands the execution of several experiments.
Of particular importance is the employment of both equilibrium
and kinetic experiments to address quantitatively the simplest reaction scheme that adequately describes the observed reaction
[1]. Furthermore, by employing site directed mutagenesis it is
possible to further verify the proposed scheme, as well as to infer
the structural features of the identified folding intermediate, as well
as the intervening transition states [2].
PDZ domains represent a class of globular domains displaying a
conserved structure composed of five to six β-strands and two
α-helices [3]. Because of their small size, good expression yields,
high solubility and reversible folding, they proved as very good
candidate to perform folding studies. In fact, the folding mechanism of PDZ domains has been studied extensively and different
members of this protein family have been characterized in detail
[4–12].
Because of their complexity, the analysis of the equilibrium and
kinetic folding mechanism of PDZ domains represent a good
example to illustrate the classical approaches that are employed to
describe the folding of single domain proteins. By taking PDZ
domains as a prototypical case, here we recapitulate the standard
experiments that are generally performed.
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_9, © Springer Science+Business Media, LLC, part of Springer Nature 2021
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150
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Candice Gautier and Stefano Gianni
Materials
2.1 Site-Directed
Mutagenesis
Site mutations is critical in understanding protein folding and in
addressing the structural features of transiently accumulated state.
Consequently, these issues have been previously discussed extensively [2, 13]. In brief, we here recall how the introduction of
nonnative interactions and steric hindrance should be avoided, as
well as any destabilizing charge. The mutagenesis can be done with
Quick-Change Lightning Site-Directed Mutagenesis kit (Agilent
Technologies) according to the manufacturer’s instructions and all
mutations must be then be confirmed by DNA sequencing. If
needed, it is possible to perform side directed mutagenesis to
introduce a tryptophan as an optical probe for fluorescence monitoring experiments.
2.2 Equilibrium
Unfolding Experiments
Equilibrium unfolding experiments are performed on a standard
spectrofluorometer, equipped with two monochromators but in
the excitation and in the emission paths. Tryptophan containing
PDZ proteins are excited at 280 nm, at a constant concentration
ranging around the μM scale, and emission spectra are recorded
between 300 and 400 nm, at increasing denaturant concentration.
Experiments can be performed at varying temperatures, using a
quartz cuvette with a path length of 1 cm. The fluorescence emission is then analyzed quantitatively following a two-state model.
2.3 Folding Kinetic
Experiments
Rapid mixing for kinetics folding and unfolding experiments can be
carried out on a stopped-flow device with an excitation wavelength
of 280 nm. An appropriate cut-off glass filter must be employed to
measure fluorescence emission. The protein concentration should
be in the μM range and the denaturant concentration may typically
vary between 0 and 8 M. Degassing solutions may help and reduce
the presence of mixing bubbles during stopped-flow experiments.
3
Methods
Protein folding can be studied in vitro by subjecting the protein to
changing condition, with the goal to infer a progressive denaturation. The loss of native structure can be monitored by measuring
changes in absorbance or fluorescence as well as using circular
dichroism and NMR spectroscopy. Denaturants like guanidinium
or urea are most commonly used to study protein folding because
they disrupt a large number of weak interactions, which allows us to
study the progressive loss of structure by varying concentrations of
denaturant [14]. Denaturation can also be caused by a change of
temperature and pH [1]. In these cases, however, the dependence
of the unfolding free energy is more complex and, therefore, we will
not discuss it in this brief Methods.
PDZ Domain Folding
3.1 Equilibrium
Denaturation
Experiments Induced
by Chaotropic Agents
151
1. Prepare two solutions containing the same concentration of
protein and the same buffer, one of them with a denaturant at
the highest concentration you aim to reach for the experiment.
Mix different volumes of the denaturing solution to the native
solutions in order to obtain increasing concentrations of denaturant. Let the solution equilibrate for minutes. Measure the
optical properties of the solutions (fluorescence, absorbance,
CD spectrum) at a given excitation wavelength (see Note 1).
2. To analyze the equilibrium transition make a plot of the optical
properties of the solution as a function of denaturant concentration. Folding is generally a cooperative process that typically
returns an all-or-none effect represented by a sigmoidal shape
[15]. Figure 1 exemplifies the urea induced equilibrium transition of the second PDZ domain from PTP-BL measured at
350 nm in the presence of phosphate buffer pH 7.2 at 25 C.
If the protein displays a two-state transition it can be postulated that
DÐN
K eq ¼ ½D =½N and
ΔG DN ¼ RT ln K eq :
where Keq is the equilibrium constant of the reaction, ΔGD-N is
the free energy of the unfolding reaction, R is the gas constant,
T is the temperature, and [D] and [N] are the concentrations of
the unfolded and folded state, respectively, at that condition.
A useful way to address quantitatively equilibrium experiments is to assume a linear free-energy relationship whereby it
can be assumed that
ΔG DN ¼ ΔG 0DN m U N ½denaturant
where ΔG0D-N is the free energy or stability of the protein in
absence of denaturant [14]. It can be calculated by extrapolating to zero the linear plot of the free energy of the unfolding
reaction ΔGD-N against varying concentrations of denaturant.
The m value is a constant reflecting the dependence of free
energy on denaturant concentration and depends on the
change in accessible surface area of the protein upon denaturation [14] (see Note 2).
3. To extract a quantitative analysis from the measured optical
parameters it is needed to fit the experimental data to the
(un)folding equilibrium curve. The curve is defined as follows:
Y obs ¼ ðY N þ Y D Þ
e mDN ð½urea½urea1=2 Þ
1 þ e mDN ð½urea½urea1=2 Þ
4. To test the robustness of the two-state equilibrium (un)folding
it may be appropriate to fit different wavelengths and/or data
measured with different optical probes, recorded at the same
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Candice Gautier and Stefano Gianni
Fig. 1 Equilibrium unfolding of the second PDZ domain from PTP-BL monitored
by fluorescence. The measurements were made at 350 nm in the presence of
phosphate buffer pH 7.2 at 25 C and the (un)folding equilibrium curve can be
fitted. The quantitative analysis of the observed spectroscopic signals as a
function of denaturant allows for estimation of stability of the protein in the
absence of denaturant
experimental conditions. In agreement with the two-state
assumption, the m value and the apparent transition midpoint
should be robust and conserved at all the conditions explored.
Failure to fulfil this observation demands additional care and
can imply the presence of folding intermediates that accumulate at equilibrium.
3.2
Kinetic Studies
Kinetic folding studies are critical to understand the protein
dynamics and the mechanical details of the folding pathway. Furthermore, they are essential to address the structural properties of
folding transition states as well as in defining which amino acids are
important for the folding of a given protein.
In general, any kinetic technique implies the rapid perturbation
of the equilibrium by changing the chemical (mixing techniques) or
physical (relaxation techniques) properties of the solution containing the protein of interest.
In this section we will briefly describe the experiments obtained
on the third PDZ from PSD-95 [4], using the stopped-flow methodology, which represents the most versatile method for these types
of experiments.
PDZ Domain Folding
153
Fig. 2 Representative folding (grey) and unfolding (black) kinetic traces recorded
for the third PDZ from PSD-95 following the change in tryptophan fluorescence,
the lines are the best fit to a single exponential decay
1. The reaction is made by rapidly mixing the unfolded protein
into a buffer that is suitable for folding (folding studies) and
vice versa by rapidly mixing the folded protein into a denaturant buffer (unfolding studies). Experimentally, these studies are
made using a stopped-flow device with pressure-driven syringes
pushing the two solutions to a mixing chamber in which the
reaction can be monitored by fluorescence. A typical stoppedflow trace is shown in Fig. 2 (see Note 3).
In a two-state reaction, the traces are the best fit to a single
exponential decay and the observed rate constant kobs is
kobs ¼ k0F exp ðm F ½denaturantÞ þ k0U exp ðmU ½denaturantÞ
kobs ¼ kF þ kU
where k0F and k0U are the folding and unfolding rate constants in the
absence of denaturant, kF and kU the folding and unfolding rate
constants, and mF and mU are the m-values of folding and unfolding [15] (see Notes 4 and 5).
A logarithmic plot of the observed rate constants against varying denaturation concentrations is used to describe the kinetics of
protein folding and is called a chevron plot (see Fig. 3).
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Candice Gautier and Stefano Gianni
a
b
3
2.5
TS
kobs (S–1)
2
1.5
1
U
0.5
0
0
N
1
2
3
4
5
6
7
8
[Urea] (M)
Fig. 3 Typical chevron plot for a two-state mechanism represented by the folding kinetics of the third PDZ from
PSD-95 in presence of phosphate buffer pH 7.2 at 25 C (A) and energy diagram for the folding of a typical
two-state folding mechanism (B)
The m values represent the slopes of the folding (mf) and
unfolding (mu) branches of the chevron plots and can be used to
estimate the position of the transition state along the folding reaction coordinate.
However, most PDZ domains present a folding profile consistent with a three-state model presenting a high-energy folding
intermediate between two transition states. When a folding intermediate is present the observed rate constant becomes dependent
on more than one energy barrier and refolding is generally
described by the sum of two or more exponential processes
[16]. In that case the appearance deviates from the classical
V-shaped chevron and a roll-over effect is observed (see Fig. 4).
In a three-state reaction, the traces are the best fit to a single
exponential decay and the observed rate constant kobs is
kobs ¼
k0F exp ðmF ½denaturantÞ k0U1 exp ðmU1 ½denaturantÞ
K 0part exp mpart ½denaturant
kobs ¼ kF þ
kU1
1 þ K part
where kF is the folding rate constant and Kpart is a partition constant
between kU1 and kU2, the two unfolding rate constants referring to
the denatured-like and native-like transition states TS1 and TS2,
respectively. kU2 is equal to kU1/Kpart, and mF and mU are the mvalues of folding and unfolding.
Another method to identify a high-energy intermediate is to
compare the values of the equilibrium constant of the reaction Keq
obtained from the equilibrium unfolding experiments and the one
PDZ Domain Folding
a
155
b
3
2.5
TS1
2
kobs (S–1)
TS2
1.5
1
0.5
U
0
N
–0.5
–1
0
1
2
3
4
5
6
7
8
[Urea] (M)
Fig. 4 Typical chevron plot for a three-state mechanism represented by the
folding kinetics of the third PDZ from PSD-95 in presence of potassium formate
buffer pH 2.85 at 25 C (a) and energy diagram for the folding of a typical threestate folding mechanism (b)
obtained by calculating the ratio of the folding rate constant and
unfolding rate constant obtained with the kinetic folding experiment at the same conditions. They must return the same equilibrium constant [1].
4
Notes
1. Some proteins can be photosensitive. In these cases, fluorescence emission might decrease in time, when the sample is
exposed to emission light. A test can be made by comparing
the emission spectra of the same sample, when measured multiple times. If this effect is present, it might be limited by
closing the slit in the emission monochromator.
2. Reversibility must be checked by repeating equilibrium experiments and by confronting folding and unfolding independent
equilibrium experiments.
3. In a stopped-flow device the pressure hold should be used for
acquisition times bellow 1 s. The typical dead-time of a
stopped-flow device is 1–2 ms.
4. It is known that in some cases folding might be affected by
transient aggregation effect. Therefore, folding studies should
be carried out at different protein concentration, at the same
experimental conditions, to exclude protein aggregation
effects.
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Candice Gautier and Stefano Gianni
5. Proline cis–trans isomerization is a slow event that can occur in
the denatured state of proteins. In these cases, refolding timecourse typically displays slow phase(s) that are directly associated with proline cis–trans isomerization.
References
1. Fersht A (1999) Structure and mechanism in
protein science. Freeman W.H. and Co,
New York
2. Fersht AR, Matouschek A, Serrano L (1992)
The folding of an enzyme. I. Theory of protein
engineering analysis of stability and pathway of
protein folding. J Mol Biol 224:771–782
3. Doyle DA, Lee A, Lewis J, Kim E, Sheng M,
MacKinnon R (1996) Crystal structures of a
complexed and peptide-free membrane
protein-binding domain: molecular basis of
peptide
recognition
by
PDZ.
Cell
85:1067–1076
4. Calosci N, Chi CN, Richter B, Camilloni C,
Engstrom Å, Eklund L, Travaglini-AllocatelliC, Gianni S, Vendruscolo M, Jemth P (2008)
Comparison of successive transition states for
folding reveals alternative early folding pathways of two homologous proteins. Proc Natl
Acad Sci U S A 105:19241–19246
5. Chi CN, Gianni S, Calosci N, TravagliniAllocatelli C, Engstrom Å, Jemth P (2007) A
conserved folding mechanism for PDZ
domains. FEBS Lett 581:1109–1113
6. Gianni S, Ivarsson Y, De Simone A, TravagliniAllocatelli C, Brunori M, Vendruscolo M
(2010) Structural characterization of a misfolded intermediate populated during the folding process of a PDZ domain. Nat Struct Mol
Biol 17:1431–1437
7. Hultqvist G, Pedersen SW, Chi CN,
Strømgaard K, Gianni S, Jemth P (2012) An
expanded view of the protein folding landscape
of PDZ domains. Biochem Biophys Res Commum 421:550–553
8. Ivarsson
Y,
Travaglini-Allocatelli
C,
Brunori M, Gianni S (2008) Folding and
misfolding in a naturally occurring circularly
permuted PDZ domain. J Biol Chem
283:8954–8960
9. Ivarsson Y, Travaglini-Allocatelli C, Jemth P,
Malatesta F, Brunori M, Gianni S (2007) An
on-pathway intermediate in the folding of a
PDZ domain. J Biol Chem 282:8568–8572
10. Jemth P, Gianni S (2007) PDZ domains: folding and binding. Biochemistry 46:8701–8708
11. Gianni S, Geierhaas CD, Calosci N, Jemth P,
Vuister
GW,
Travaglini-Allocatelli
C,
Vendruscolo M, Brunori M (2007) A PDZ
domain recapitulates a unifying mechanism
for protein folding. Proc Natl Acad Sci U S A
104:128–133
12. Di Silvio E, Brunori M, Gianni S (2015) Frustration sculpts the early stages of protein folding.
Angew
Chem
Int
Ed
Engl
54:10867–10869
13. Fersht AR, Sato S (2004) Phi-value analysis and
the nature of protein-folding transition states.
Proc Natl Acad Sci U S A 101:7976–7981
14. Myers JK, Pace CN, Scholtz JM (1995) Denaturant m values and heat capacity changes: relation to changes in accessible surface areas of
protein unfolding. Protein Sci 4:2138–2148
15. Jackson SE, Fersht AR (1991) Folding of chymotrypsin inhibitor 2. 1. Evidence for a
two-state
transition.
Biochemistry
30:10428–10435
16. Parker MJ, Spencer J, Clarke AR (1995) An
integrated kinetic analysis of intermediates
and transition states in protein folding reactions. J Mol Biol 253(5):771–786
Chapter 10
Development of Peptide-Based PDZ Domain Inhibitors
Dominik J. Essig, Javier R. Balboa, and Kristian Strømgaard
Abstract
Over the past decades, peptide-based drugs have gained increasing interest in a wide range of treatment
applications, primarily because of high potency and selectivity, as well as good efficacy, tolerability, and safety
often achieved with peptides. Attempts to target postsynaptic density protein of 95 (PSD-95) PSD-95/
Discs large/Zonula occludens-1 (PDZ) domains, which mediate the formation of a ternary complex with
the N-methyl-D-aspartate (NMDA) receptor and neuronal nitric oxide synthase (nNOS) responsible for
excitotoxicity in ischemic stroke, by high-affinity small molecules have failed in the past. In this chapter, we
focus on the discovery of peptide-based drugs targeting PSD-95, using AVLX-144 as an example, from the
synthesis, over binding assays to its target, to further in vitro experiments based on the development of
AVLX-144, a potential stroke treatment, which is planned to enter clinical trials in 2020.
Key words PDZ domains, Protein–protein interactions, Inhibition, Stroke, AVLX-144
1
Introduction
Protein–protein interactions (PPIs) are highly abundant regulatory
mechanism in biological systems, which mediate a broad range of
cellular processes, including signal transduction, cellular communication, metabolism, gene expression, and membrane transport.
Hence, dysfunction or alterations of PPIs are linked to various
diseases states, and inhibition of PPIs possesses enourmous opportunities in drug discovery [1–3]. Many PPIs are mediated by
specialized protein domains, one of the most abundant being postsynaptic density protein-95/Discs large/Zonula occludens-1
(PDZ) domains.
PDZ domains consist of 80–90 amino acids and are present in
great number in multicellular organisms; in humans 267 different
PDZ domains are found in over 150 different proteins [4, 5]. PDZ
domains are usually part of scaffold and adaptor proteins involved
in the assembly of cellular signaling complexes, typically by recognizing the C-terminal of their interacting partners. Inhibition of
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_10, © Springer Science+Business Media, LLC, part of Springer Nature 2021
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Dominik J. Essig et al.
PDZ-domain-mediated PPIs represents a promising strategy for
specific therapeutic intervention of signaling events rather than
targeting entire signaling cascades, as typically achieved by receptor
antagonists. PDZ domains are attractive therapeutical targets as
they are associated with a wide range of diseases and disorders
ranging from neurodegenerative diseases and mental disorders to
cancer [6, 7]. However, drugging PDZ domains have been shown
to be challenging as they are highly promiscuous and peptide drug
affinities tend to range in the low micromolar range [8, 9].
A successful story on targeting PDZ mediated interactions is
the development of NA-1 (Tat-NR2B9c, nerinetide), which published their phase III clinical trials in February 2020 [10], as well as
AVLX-144 (UCCB01–144), both targeting the PDZ domains of
the postsynaptic density protein of 95 (PSD-95) as potential stroke
treatment. During cerebral ischemia, an excessive amount of glutamate is released from the presynapse into the synaptic cleft. The
released glutamate hyper-activates the N-methyl-D-aspartate
(NMDA) receptors in the postsynaptic membrane, which leads to
increased influx of Ca2+ ions into the postsynapse. Ca2+ ions are
required to activate the calmodulin-dependent enzyme neuronal
nitric oxide synthase (nNOS), which forms a ternary complex with
the NMDA receptor and PSD-95, and generates nitric oxide
(NO) in excess that ultimately leads to excitotoxicity. The proximity
provided by the complex is crucial for NO production, and it is
mediated by the PSD-95 PDZ domains 1 and 2 [11–13].
Several attempts to inhibit generation of NO in cerebral ischemia have been made by targeting upstream and downstream proteins of PSD-95. For example, by competitive or noncompetitive
NMDA receptor antagonists [14–17], which blocks glutamatemediated activation of NMDA receptors and therefore inhibit
influx of Ca2+ [18]. However, blocking the NMDA receptor has
severe side effects such as induced impairment of key brain functions, including sedation and psychotomimetic side effects. Furthermore, NMDA receptor agonists have a short therapeutic
window for drug administration, as they are effective only when
administrated before or shortly after a stroke [19]. Therefore, to
block the downstream interaction between the PDZ domains of
PSD-95 and their interaction partners nNOS and the NMDA
receptor has proven to prevent ischemic brain damage in both
rodents and nonhuman primates [20].
Targeting the PDZ domains of PSD-95 by small moleculebased inhibitors has been unsuccessful. Binding affinities did not
exceed the micromolar range, which is probably due more distant
key interaction points along the binding pocket compared to traditional small molecule targets [1]. PDZ domain binding typically
happens when an antiparallel β-strand ligand interacts with the βB
strand and the αB helix of the PDZ domain, making the PDZ
domain binding pocket especially large. Moreover, there are some
PDZ Domain Peptide Inhibitors
159
Fig. 1 (I) Trimeric complex formed by PDZ-mediated interactions with the C-terminal tail of GluN2B, an NMDA
receptor subunit with nNOS, and the PDZ domains of PSD-95. This complex produces excitotoxic nitric oxide
(NO) upon influx of calcium ions. (II) Dimeric inhibitor AVLX-144 blocks the complex formation and thereby
prevents formation of NO. (III) Structures of PDZ inhibitors of NA-1 and AVLX-144
specific linear motifs necessary for PDZ domain recognition that
provide key hydrogen bonds for the interaction. This set of characteristics is not easily mimicked by small molecules and this eventually led to shift to peptide based inhibitors [21]
Peptide-based inhibitors are more suited to target this interaction due to their size and flexibility [22, 23], and are currently on
the rise from preclinical studies toward Phase III clinical trials. The
first attempt targeting the NMDA/PSD-95/nNOS complex with a
peptide-based inhibitor, was the 20-mer peptide NA-1 in which the
C-terminal 9 amino acids from the NMDA receptor subunit
GluN2B was fused to an 11 amino acid HIV-1-derived peptide
moiety (Tat), which facilitates penetration of cell membranes and
the blood–brain barrier [20]. NA-1 has proven to have an extensive
neuroprotective effect in rats and nonhuman primate ischemic
stroke models with a 1–3 h time window and the Phase III clinical
trials results were published recently (see Fig. 1) [10].
Nonetheless, NA-1 has a relatively low affinity toward its target
protein, which was later overcome by developing a dimeric inhibitor AVLX-144 designed by Bach, Strømgaard, and collaborators,
which block PSD-95 PDZ1 and 2 simultaneously. This design leads
to a 1000-fold increase in binding affinity to PSD-95 relative to
NA-1 [24]. Here we detail the process of peptide-based drug
development based on the development of AVLX-144, from standard solid-phase peptide synthesis to advanced in vitro and in vivo
screening assays to elucidate the pharmacological behavior of
drugs.
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Dominik J. Essig et al.
Materials
Prepare all solutions using ultrapure water (prepared by purifying
deionized water, to attain a sensitivity of 18 MΩ-cm at 25 C) and
analytical grade reagents (standard organic solvents are not listed in
the Material section). Prepare and store all reagents at room temperature (unless indicated otherwise).
2.1 Peptide
Synthesis
1. Silicycle MiniBlock® 10 mL polypropylene disposable reaction
tubes (SiliCycle Inc., Québec, Canada) or equivalent.
2. Wang resins preloaded with the C-terminal amino acid or
2-chlorotrityl chloride resin.
3. 9-Fluorenyl-methyloxycarbonyl (Fmoc)-protected amino acids
with standard side-chain protection groups.
4. 0.5 M 2-(1-benzotriazole-1-yl)-1,1,3,3-tetramethyluronium
hexafluorophosphate (HBTU) or 0.5 M 2-(1H-7-azabenzotriazole-1-yl)-1,1,3,3-tetramethyluronium
hexafluorophosphate (HATU) in dimethylformamide (DMF). Dissolve
1.9 g HBTU or 1.9 g HATU in 10 mL DMF and store
solutions in light-protected bottles.
5. 20% (v/v) piperidine in DMF.
6. Kaiser test kit (Sigma-Aldrich Corp, St. Louis, USA).
7. Capping solution: 10% (v/v) acetic anhydride, 10% (v/v) N,Ndiisopropylethylamine (DIPEA), 80% (v/v) DMF. Mix 1 mL of
acetic anhydride, 1 mL of DIPEA in 8 mL of DMF. Prepare
solution fresh before use.
8. Cleavage cocktail: 80% (v/v) trifluoroacetic acid (TFA), 10%
(v/v) water, 10% (v/v) Triisopropylsilane (TIPS).
9. Ice-cold diethyl ether: store at 20 C until usage.
10. Cy5-Maleimide (GE Healthcare, Chicago, USA).
11. Polyethylene glycol (PEG)-diacid (Iris Biotech GmbH, Marktredwitz, Germany).
12. Biotin (lyophilized powder).
13. PBS buffer: 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4,
2 mM KH2PO4, pH 7.4. Dissolve 8 g NaCl, 200 mg KCl,
1.44 g Na2HPO4, and 240 mg KH2PO4 in 800 mL distilled
water. Adjust the pH to 7.4 by adding either 0.1 M NaOH or
0.1 M HCl. Fill up to a total volume of 1 L with distilled water.
14. Triphenylphosphine (PPh3).
15. Diisopropyl azodicarboxylate (DIAD).
16. Diazabicyclo[5.4.0]undec-7-ene (DBU).
17. 2,4,6-trimethylpyridine (Collidine).
PDZ Domain Peptide Inhibitors
2.2 Fluorescence
Polarization
161
1. Fluorescence polarization (FP) buffer: 50 mM sodium phosphate (NaPi) pH 7.5. Dissolve 6.4 g of Na2HPO4 and 1.58 g
of NaH2PO4 in 800 mL distilled water. Adjust the pH to 7.5
by adding either 0.1 M NaOH or 0.1 M HCl. Fill up to a total
volume of 1 L with distilled water.
2. Cy5 conjugated peptide probe: prepared as 5 μM stock in FP
buffer
3. 384-well plate, black flat-bottom
4. Analytical pipette channels, with low protein binding pipette
tips
5. 10 bovine serum albumin (BSA) stock: Dissolve 1 g BSA in
10 mL FP buffer
2.3 Isothermal
Calorimetry
1. PBS buffer (see Subheading 2.1). Buffer is degassed and
filtered.
2. Protein and peptide samples dissolved in the same exact PBS
buffer.
2.4 Pull-Down
of Nonischemic Brain
Lysates
1. Biotin-labeled compound (see Subheading 3.1 and Note 1).
2. 10–12-week old C57BL/6JBabr mice (Charles River Laboratories, Margate, UK).
3. Streptavidin-conjugated resin (Thermo Scientific, Loughborough, UK).
4. Immobilization/Homogenization buffer: 1 M Tris–HCl
pH 7.0 containing 0.05% sodium deoxycholate. Dissolve
121.14 g of Tris-base in 800 mL distilled water. Adjust the
pH to 7.0 by adding 0.1 M HCl. Fill up to a total volume of 1 L
with distilled water and add 0.5 g sodium deoxycholate.
5. Sample Buffer: 4% sodium dodecyl sulfate (SDS), 20% glycerol,
10% 2-mercaptoethanol, 0.004% bromophenol blue, and
0.125 M Tris–HCl pH 6.8. Dissolve 0.4 g SDS, 2 mL glycerol,
1.25 mL of 1 M Tris–HCl (pH 6.8), and 20 mg bromophenol
blue in 10.7 mL distilled water, or purchase premixed sample
buffer (Sigma-Aldrich Corp., St.Louis, USA).
6. Pierce™ bicinchoninic acid assay (BCA) protein quantitation
kit (Thermo Scientific, Loughborough, UK).
7. Pierce™ ECL Western Blotting Substrate (Thermo Scientific,
Loughborough, UK).
8. β-mercaptoethanol.
9. PSD-95 monoclonal antibody (cat. No. MA1-045, Thermo
Fisher, Loughborough, UK).
10. PSD-93 monoclonal antibody (cat. No. 75-057; NeuroMab,
Davis, USA).
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Dominik J. Essig et al.
11. SAP-97 monoclonal antibody (cat. No. 75-030; NeuroMab,
Davis, USA).
12. SAP-102 polyclonal antibody (cat. No. 124202; Synaptic Systems, Goettingen, Germany).
13. Horseradish peroxidase (HRP) secondary antibody.
2.5 Human Blood
Plasma Stability Assay
1. Compound (2.5 mM stock solution in water).
2. Human blood plasma, pooled (3H Biomedical, Uppsala,
Sweden).
3. 5% (w/v) aqueous trichloroacetic acid.
2.6 In Vitro Toxicity
Experiment (MTT
Assay)
1. Compound
2. MK-801 (Tocris Bioscience, Bristol, UK)
3. 5 mM sodium azide in Hank’s Balanced Salt Solution (HBSS)
or acetic acid
4. N-methyl-D-aspartic acid (NMDA)
5. 5 mg/mL 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) in PBS buffer.
2.7
Equipment
1. Agilent 6410 Triple Quadrupole Mass Spectrometer with electron spray ionization (ESI) coupled to an Agilent 1200 HPLC
system (ESI-LC/MS), equipped with C18 reverse phase columns: Zorbax Eclipse XBD-C18, 4.6 mm 50 mm for the
analysis of peptide and Agilent Poroshell 300SB-C18,
2.1 mm 75 mm for the characterization of protein (Agilent
Technologies, Santa Clara, USA)
2. Waters ACQUITY system for analytical reverse phase
ultrahigh-performance liquid chromatography (RP-UPLC)
with a C18 reverse phase column (ACQUITY UPLC BEH
C18 1.7 μm, 2.1 mm 50 mm) for the analysis of peptides
and a C8 column (ACQUITY UPLC BEH C8 1.7 μm,
2.1 mm 50 mm) for the analysis of proteins (Waters Corp.,
Milford, USA)
3. Waters 2545Q system for preparative reverse phase highperformance liquid chromatography (RP-HPLC) (Waters
Corp., Milford, USA) with a C18 reverse phase column Zorbax
300 SB-C18, 21.2 mm 250 mm (Agilent Technologies,
Santa Clara, USA) for the purification of peptides and a reverse
phase C4 column 250 10 mm (Jupiter®, Phenomenex,
Torrance, USA) for the purification of proteins.
4. ÄKTA explorer 100 FPLC system (GE Healthcare, Chicago,
USA), equipped with HiLoad® 16/600 Superdex® 75 pg
column (GE Healthcare, Chicago, USA) for size-exclusion
chromatography (SEC)
PDZ Domain Peptide Inhibitors
163
5. Safire2 ™ plate reader (Tecan, M€annedorf, Switzerland)
6. MicroCal iTC200 (Malvern Pananalytical Ltd., Malvern, UK)
7. MiniSpin® centrifuge (Eppendorf, Hamburg, Germany)
8. ImageQuant LAS 4000 (GE Healthcare, Chicago, USA)
9. ELISA microplate reader (Molecular Devices, San Jose, USA)
10. ELISA plate shaker
11. CO2 INCUBATOR REVCO ULTIMA for shaking at 37 C in
a humidified atmosphere of 5% CO2
12. Sorvall LYNX 6000 Superspeed Centrifuge
3
Methods
Here we describe the step-by-step procedure of the development of
AVLX-144, a potential stroke candidate, from the screening to
in vitro studies. This method section should inspire and provide
the reader an overview of peptide-based drug development.
3.1 Solid-Phase
Peptide Synthesis
Standard Fmoc peptide synthesis is described here to generate the
free carboxyl peptides. While there are two different strategies for
peptide synthesis, Fmoc and tert-butyloxycarbonyl (Boc), the latter
has harsher reaction conditions, especially during the cleavage step
where hydrofluoric acid (HF) must be used. On the other hand,
Fmoc chemistry offers milder conditions and also the chance to
follow up the reaction by spectrophotometric monitoring of the
dibenzofulvene, the removal product of the Fmoc protection
group.
In this method section we are going to tackle the standard
synthesis of peptide fragments able to bind to PDZ domains and
the generation of fluorescent peptide probes with Cy5-maleimide
for fluorescence polarization assays.
3.1.1 Synthesis of PDZ
Peptide Binders
1. For the synthesis of free carboxy PDZ binders we use preloaded Wang resin (see Note 2).
2. In a 10 mL disposable reaction tube, transfer 0.1 mmol of resin
(see Note 3) and wash with 5 mL of dichloromethane (DCM)
followed by 10 mL of DMF. Add 2 mL of DMF and cap the
tube. Allow the resin to swell for 30 min (see Fig. 2, step 1).
3. Drain solvent and resuspend resin with 2 mL of 20% (v/v)
piperidine in DMF. Incubate on a MiniBlock® (500 rpm) at
room temperature for 5 min. Drain solvent and wash resin with
20 mL of DMF. Repeat this procedure one more time.
4. Taking into account the synthesis scale (0.1 mmol g1), dissolve 4 eq of Fmoc-amino acid in 4 eq HBTU solution (see
Note 4). Vortex the solution until its complete solution.
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Dominik J. Essig et al.
Fig. 2 General synthesis scheme for Fmoc SPPS that can be divided in: addition
of the first amino acid to the resin (steps 1–2), Fmoc deprotection and
PDZ Domain Peptide Inhibitors
165
5. Add 4 eq DIPEA to the amino acid solution and vortex it. Incubate 2–3 min to preactivate the building block for efficient
coupling.
6. Transfer amino acid solution to the reaction tube, close with a
septum and Incubate at room temperature on the MiniBlock®
at 500 rpm for 1 h (see Fig. 2, step 2).
7. Check completion of the coupling reaction using Kaiser test,
and proceed to step 8 only when coupling is complete (see
Note 5). If coupling is incomplete repeat steps 4–7
8. Wash the resin extensively with 20 mL of DMF. Repeat steps
2–7 (see Fig. 2, steps 3–6) for the next amino acid until all
building blocks have been coupled (see Note 6).
9. After extensive washing with 20 mL of DMF and 20 mL of
DCM dry the resin under vacuum
10. Add 5 mL cleavage cocktail to the reaction tube and incubate at
room temperature on a MiniBlock® at 500 rpm for 2 h (see
Note 7) (see Fig. 2, steps 7 and 8).
11. Filter resin by using air to push cleavage mixture through the
disposable reaction tubes and collecting flow-through, which
contains peptide and cleavage cocktail, in a sterile 50 mL
Falcon tube.
12. Precipitate peptide by adding 50 mL ice-cold diethyl ether, and
centrifuge for 10 min at 3500 g, in 4 C. Discard supernatant. Repeat this step 3 times.
13. Dissolve pellet with water. If peptide is not soluble use the
minimal amount of dimethyl sulfoxide (DMSO) (see Note 8)
or acetonitrile (MeCN), and purify using preparative
RP-HPLC with a linear gradient of a binary solvent system of
H2O–MeCN–TFA (A: 95/5/0.1; B: 5/95/0.1) at a flow rate
of 20 mL min1.
14. Confirm final peptide mass with LC-MS using reverse-phase
C18 column for peptide analysis, operating at a linear gradient
binary solvent system of H2O–MeCN–formic acid (A: 95/5/
0.1; B: 5/95/0.086) at a flow rate of 1 mL min1.
15. Analyze peptide purity with RP-UPLC using the C18 reverse
phase column for peptide analysis and a linear gradient of the
binary solvent system of H2O–MeCN–TFA (A: 95/5/0.1; B:
5/95/0.1) at a flow rate of 1 mL min1.
16. Lyophilize the final product.
ä
Fig. 2 (continued) subsequent amino acid additions (steps 4–6) and final Fmoc
deprotection and cleavage from the resin (steps 7–8). Fmoc group, temporary
protecting groups (TPG) and resin linker are highlighted in red, blue and green
respectively. AAn and Rn represent an amino acid and its side chain in a specific
n position
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Dominik J. Essig et al.
3.1.2 Generation
of Peptide Probes:
Conjugation with Cy5
Maleimide
1. For the synthesis of peptides probes we follow the procedure
described in Subheading 3.1.1 until step 8, were we add a CSG
spacer to the total peptide sequence.
2. The peptide is then cleaved, purified, and lyophilized following
the steps 10–15 described in Subheading 3.1.1
3. Degassed PBS buffer is then used to dissolve the desired
amount of peptide. To degas the buffer seal the flask with a
Septa and place a long syringe in the bottom of the flask with
N2 or He sparging and leave another small syringe in the top to
allow the excess of N2 to get removed.
4. 10 eq of Cy5-maleimide is then dissolved in DMSO and added
to the peptide solution in PBS. Leave the reaction for 2 h at
room temperature.
5. The peptide mixture is then purified using preparative
RP-HPLC with a linear gradient of a binary solvent system of
H2O–MeCN–TFA (A: 95/5/0.1; B: 5/95/0.1) at a flow rate
of 20 mL min1 (see Note 9)
6. Confirm the mass of the final product with LC-MS using
reverse-phase C18 column for peptide analysis, operating at a
linear gradient binary solvent system of H2O–MeCN–formic
acid (A: 95/5/0.1; B: 5/95/0.086) at a flow rate of 1 mL min
1
. Analyze peptide purity with RP-UPLC using the C18
reverse phase column for peptide analysis and a linear gradient
of the binary solvent system of H2O–MeCN–TFA (A: 95/5/
0.1; B: 5/95/0.1) at a flow rate of 1 mL min1.
7. Lyophilize the final product (see Note 10).
3.2 Dimeric PDZ
Peptides and Cell
Penetrating Tags
Compared to monomeric peptides, dimerization of peptides has
several advantages, such as increased binding affinity or enhanced
selectivity against a specific protein. Below we provide a general
approach on how to synthesize dimeric peptide inhibitors based on
the usage of standard Fmoc SPPS as described in Subheading 3.1.1.
Moreover, cell penetrating peptide (CPP) tags facilitate the
cellular uptake of therapeutical binders or “cargo” them inside
cells. These CPPs tags are usually enriched in positively charged
amino acids or have sequences with an alternating pattern of polar/
charge amino acids or nonpolar/hydrophobic combinations.
In the section below we explain how to synthesize dimeric PDZ
peptides and how to include a CPP tag to them.
3.2.1 Synthesis
of Dimeric Symmetrical
PDZ Binders
1. Follow the steps in Subheading 3.1.1 until step 8, until the
pentameric binder is completed on resin and remove the Fmoc
group by using 20% (v/v) piperidine in DMF (see Note 11).
2. The PEG-diacid (0.1 eq) (see Note 12) is preactivated with
HBTU (0.2 eq) and DIPEA (0.4 eq) and added to the peptideresin (1 eq, 0.25 mmol) in DMF (2 mL), and incubated for
PDZ Domain Peptide Inhibitors
167
30 min, followed by washing with DMF. This procedure is
repeated 5 times.
3. Add 5 mL cleavage cocktail to the reaction tube and incubate at
room temperature on a MiniBlock® at 500 rpm for 2 h.
4. Filter resin by using air to push cleavage mixture through the
disposable reaction tubes and collecting flow-through, which
contains peptide and cleavage cocktail, in a sterile 50 mL
Falcon tube.
5. Precipitate peptide by adding 50 mL ice-cold diethyl ether, and
centrifuge for 10 min at 3500 g, in 4 C. Discard supernatant. Repeat this step 3 times.
6. Dissolve pellet with water. If peptide is not soluble use the
minimal amount of DMSO or MeCN, and purify using preparative RP-HPLC with a linear gradient of a binary solvent
system of H2O–MeCN–TFA (A: 95/5/0.1; B: 5/95/0.1) at
a flow rate of 20 mL min1.
3.2.2 Synthesis of
the Dimeric Peptide Linker,
Ns-NPEG4 Linker
[10-((2-Nitrophenyl)
Sulfonyl)4,7,13,16-Tetraoxa-10Azanonadecane-1,19-Dioic
Acid] for CPP Conjugation
1. Wash the 2-chlorotrityl chloride resin (3 mmol) with DMF
(15 times, each 10 mL). Finally, swell in 15 mL of DMF for
20 min, drain, and treat with Fmoc-NH-PEG2CH2CH2COOH (2 mmol) in dry DMF (8 mL) and DIPEA
(10 mmol). After shaking for 60 min, add MeOH (25 mmol)
(see Fig. 3, step 1).
2. Drain and wash the resin with DMF (5 times, each of 10 mL),
and deprotect the Fmoc group with 20% (v/v) piperidine in
DMF (15 mL) for 5 min and 15 min. Wash in between with
DMF and tetrahydrofuran (THF) (5 times, each of 10 mL) (see
Fig. 3, step 2.1)
3. Swell the resin in DIPEA (12 mmol) and THF (8 mL) for
15 min, and add slowly ortho-nitrobenzenesulfonyl chloride
(NsCl, 8 mmol) in DCM (5 mL) while gently stirring the resin.
Fig. 3 SPPS synthesis scheme of the Ns-NPEG4 linker. (1) Fmoc-NH-PEG2-CH2CH2COOH is loaded into the
resin (2) Removal of Fmoc group and addition of NsCl and PEG2-CH2CH2COOtBu. (3) Removal of tBu group and
cleavage from the resin using TFA/H2O/TIPS to obtain the final Ns-NPEG4 Linker. (Figure adapted from Bach
et al. (2012))
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Dominik J. Essig et al.
Shake for 4 h, drain and flow-wash consecutively with THF,
MeOH, DCM, and THF (see Fig. 3, step 2.2).
4. Treat the resin (2 mmol) with triphenylphosphine (PPh3,
10 mmol) in THF (5 mL) and HO-PEG2-CH2CH2COOtBu
(10 mmol) in THF (5 mL) under N2 atmosphere. Add dropwise diisopropyl azodicarboxylate (DIAD, 10 mmol) and shake
for 1 h (see Fig. 3, step 2.3).
5. Wash the resin with THF and DCM, dry under vacuum and
treat with cleavage cocktail for 2.5 h. Collect the TFA-mixture
and wash the resin with TFA and DCM. Evaporate the combined TFA/DCM fractions and coevaporate with diethyl ether
(2 30 mL) (see Fig. 3, step 3).
6. Dissolve the final product in water–MeCN (75/25, 100 mL)
and lyophilize to provide Ns-NPEG4 linker.
3.2.3 Synthesis
of TAT-N-Dimeric Peptide
1. Preactivate the Ns-NPEG4 linker (0.1 eq) with HBTU (0.2 eq)
and DIPEA (0.4 eq) in DMF. Subsequently, add the preactivated solution to the Fmoc-deprotected Wang-resin-bound
IETDV (1 eq). Shake the mixture for 45 min, drain the preactivated solution and wash with DMF (5 times, each of
10 mL). Repeat this step 5 times (see Fig. 4, step 1).
Fig. 4 SPPS synthesis scheme of the Tat-N-Dimer using the Ns-NPEG4 linker. (1) SPPS synthesis of the PDZ
binding peptide and addition of the Ns-NPEG4 linker on-resin. (2) Treatment with DBU and mercaptoethanol
and addition of Fmoc-Arg(Pbf)-OH. (3) Synthesis of the remaining Tat sequence and cleavage from the resin.
(Figure adapted from Bach et al. (2012))
PDZ Domain Peptide Inhibitors
169
2. The Ns group is removed by adding 1,8-diazabicyclo[5.4.0]
undec-7-ene (DBU, 0.5 mmol) in DMF (2 mL) and
β-mercaptoethanol (0.5 mmol) in DMF (2 mL) and shake it
for 30 min (see Fig. 4, step 2.1).
3. Wash with DMF (5 times, each of 10 mL) and repeat the
treatment with β-mercaptoethanol/DBU. Wash the resin
again consecutively with DMF, DCM, MeOH, and DCM
(5 times each, each of 10 mL). For Tat- (AVLX-144) and
ReTat-N-dimer (AVLX-147) the first amino acid from the
sequence (L- or D-Arg, respectively) was coupled to the linker
nitrogen by six consecutive couplings of Fmoc-L/D-Arg(Pbf)OH.
4. For each coupling, activate the Fmoc-L/D-Arg(Pbf)-OH
(0.5 mmol) with HATU in DMF (2 mL, 0.244 M) and collidine (132 μL) (see Fig. 4, step 2.2).
5. Remove the Fmoc protection group with 20% (v/v) piperidine
in DMF and add the remaining Tat- or ReTat sequence following the same procedures as described in Subheading 3.1.1 (see
Fig. 4, step 3).
3.3 Affinity
and Selectivity
Experiments
3.3.1 Fluorescence
Polarization Assays
The expressed PDZ domains can be used directly in any in vitro
assay for detection of protein–protein interactions. In this case, we
describe the use of an FP assay to assess the binding affinity of the
target protein with the synthesized peptide ligands. The protocol
involves a fluorescence-labeled peptide probe (in this case Cy5),
which is at a constant concentration. To the probe the protein is
titrated with increasing concentration to obtain a saturation curve.
The dissociation constant, Kd, indicates the strength of the binding
interaction between protein and ligand is interpolated from the
saturation curve.
1. Prepare a series of 12 twofold dilutions of the desired PDZ
domain, diluting from a starting concentration of 200 μM (see
Note 13) with FP buffer. For triplicate measurements, transfer
three times 15 μL of each dilution onto a 384-well black flatbottom plate.
2. In an Eppendorf tube, prepare BSA blank by diluting 10x BSA
stock solution into FP buffer. For triplicate measurements of
BSA blank and FP buffer controls, transfer three times 30 μL
BSA blank and 15 μL FP buffer, respectively, into empty wells
of the 384-well plate.
3. In an Eppendorf tube, prepare 2 probe stock by diluting 10
BSA stock and a Cy5 conjugated peptide ligand to a final assay
concentration of 200 nM with FP buffer (see Note 14). Add
15 μL of the 2 probe stock to each of the protein and buffer
control wells of the 384-well plate. Incubate plate at room
temperature for 15 min. Protect the plate from light.
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Dominik J. Essig et al.
4. Analyze samples with a Safire2 ™ plate reader and adjust instrumental z-factor at maximum signal. Calibrate g-factor with
BSA blanks and buffer controls to target an initial millipolarization (mP) of 20 mP.
5. Record measurements at excitation/emission wavelengths of
λ633 nm/λ647 nm at 25 C. After subtracting background of
20 mP, fit saturation data using one-site specific binding
model with GraphPad Prism mathematical software. Determine Kd by identifying protein concentration at halfsaturation.
3.3.2 Isothermal
Calorimetry (ITC)
ITC is another in vitro assay that provides in addition to the
binding affinity, thermodynamic data on the binding mechanism
that can be useful in ligand development. Moreover, this technique
is label free in comparison to FP where a fluorophore needs to be
conjugated to the ligand.
1. The ligand and the protein must be solubilized in the same
buffer PBS and pH adjusted to 7.4 within 0.02 pH units (see
Note 15).
2. The calorimetry experiments were performed at 25 C, by
titration of the ligand (20 2 μL injections at 180 s intervals;
stirring speed of 1000 rpm) into the PDZ solution.
3. The Experiments were designed so that c-values (c-value ¼ Ka*
[Protein]*N; where Ka is the affinity association constant,
[Protein] is the protein concentration, and N is the stoichiometry of the binding event) were generally within 1–1000.
4. Heats of dilution were determined by titrating the ligand into
buffer and buffer into protein, respectively, which were subtracted from the observed “heat values” of ligand into protein.
5. Use ORIGIN 7.0 (Malvern Pananalytical Ltd., Malvern, UK)
to determined the thermodynamic properties of ligand binding
using nonlinear least-squares fitting assuming a single-site
model.
3.4 Further
Validation
Experiments
3.4.1 Pull-Down
of Nonischemic Brain
Lysates
Pull-down assays are used to probe protein–protein interactions in a
more complex environment. The experiment is based on a bait
molecule that is immobilized on a solid support, such as dextran
or agarose, and a mixture of potential interaction partners, such as a
tissue lysate. After incubation of bait and pray, the nonbinding
molecules are washed, and the binding molecules are eluted and
further analyzed.
1. Synthesis and purification of biotin-labeled PSD-95 inhibitor
as described in Subheading 3.1.1 solid phase peptide synthesis
(see Note 16).
PDZ Domain Peptide Inhibitors
171
2. Biotin-labeled PSD-95 peptide inhibitors were bound to
streptavidin-conjugated resin by incubating 2.5 mM of each
peptide with 120 μL of resin in immobilization buffer (see Note
17).
3. Resin and peptide solution were incubated for 3 h at room
temperature before the peptide supernatant was removed and
the resin was washed with immobilization buffer and stored at
4 C.
4. The pull-down is performed using extracts from forebrains of
10–12-week-old C57BL/6JBabr mice (Charles River Laboratories, Margate, UK).
5. Homogenization of the tissue in homogenization buffer by a
tissue grinder (Kimble Chase, Vineland, USA) (see Note 18).
6. Incubate the lysate for 1 h on ice.
7. Spin down at 20,000 rpm for 20 min with an ultracentrifuge at
4 C and collect the supernatant.
8. Measure the protein concentration of the lysate using Pierce™
bicinchoninic acid assay (BCA) protein quantitation kit and
dilute the samples to 2 μg μL1
9. Peptide-coupled resin was aliquoted into 5 20 μL samples
before adding protein lysate. The lysate was added at different
protein-to-resin ratios: 1:10, 1:50; 1:100, 1:500; 1:1000 and
rotated at 4 C overnight.
10. Spin down the samples and collect the pellet from each condition, and discard the remaining supernatant.
11. Wash the resin thoroughly with homogenization buffer.
12. Elute the proteins by heating to 70 C for 30 min in sample
buffer.
13. Samples were run on SDS-PAGE gels and blots were probed
for MAGUK proteins using primary antibodies against
PSD-95, PSD-93, SAP-97, and SAP-102 (Fig. 5).
14. Incubate the SDS-PAGE with the primary antibody overnight
and subsequently apply horseradish peroxidase (HRP) secondary antibodies for 1 h at room temperature.
15. Visualize the blots with Pierce™ ECL Western Blotting Substrate on a GE Healthcare Life Science ImageQuant 4000
imager.
16. For negative control see Note 19.
3.4.2 Blood Plasma
stability Assay
An important measurement in the early-stage drug development
process is the stability of the compound in plasma. This assay is
especially important for peptide-based drugs as peptides and proteins are degraded by proteases.
Dominik J. Essig et al.
1/500
1/1000
5
6
7
8
4
5
6
7
8
8
4
5
6
7
8
8
4
5
6
7
8
1/500
4
1/100
8
1/50
1/100
ELUATE
1/50
ELUATE
1/10
BiotinTat-NR2B9c
1/1000
BiotinUCCB01144
1/10
172
4
5
6
7
4
5
6
7
8
4
5
6
7
4
5
6
7
PSD93
PSD95
SAP97
SAP102
Fig. 5 Western blots illustrating pull-down of MAGUK proteins PSD-93, PSD-95,
SAP-97 and SAP-102 by biotin-labeled UCCB01–144 and Tat-NR2B9c.
(Figure adopted from Bach et al. (2019))
1. Collect blood (approximately 200–300 μL) from individual
mice from the neck after decapitation and allow to clot for
30 min at room temperature.
2. Centrifuge the samples at 20,000 g at 4 C for 15 min.
3. Transfer the serum to a new tube and repeat step 2.
4. Aliquot the serum and store at 80 C.
PDZ Domain Peptide Inhibitors
173
5. Dissolve 30 μL of compound stock solution (2.5 mM in MQ
water) in 270 μL blood serum sample to a final concentration
of 0.25 mM (controls see Note 20).
6. Take one aliquot at timepoint zero and then after sequential
time points aliquots of 30 μL. Quench the reaction by adding
60 μL aqueous trichloroacetic acid (5% w/v) to the aliquot.
7. Vortex the quenched sample and subsequently, incubated for
15 min at 4 C.
8. Centrifuge the samples at 18,000 g at 4 C for 2 min.
9. Analyze supernatants by analytical RP-HPLC (UV218 nm) to
quantify compounds relative to timepoint zero, and also evaluated qualitatively by ESI-LC/MS to identify the compound
(m/z) in the sample.
3.4.3 In Vitro Toxicity
Measurement (MTT-Assay)
Previous treatments of stroke targeted the NMDA receptor and
thereby caused severe side effects. For an in vitro assessment of cell
toxicity of a compound we can perform an MTT
(3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetra-zolium bromide)
assay. The MTT assay is a colorimetric assay that measures the
metabolic activity of a cell, by reducing of MTT in the mitochondria to a formazan product with purple color. The purple color can
be quantified by spectrometry, which correlates with cell viability.
The MTT assay was published by Sommer et al. with the compound
AVLX-147 instead of AVLX-144. AVLX-147 has an inverted and
stereoisomeric TAT solubility tag; nonetheless, the experimental
setup remains the same [25].
1. The experiment is performed on cortical neuronal cultures
after 7–8 days of in vitro in conditioned medium (500 μL per
well) at 37 C in a humidified atmosphere of 5% CO2.
2. For a positive control expose cultures to 10 μM MK-801
(Tocris Bioscience; Bristol, UK) for complete blockage of
NMDA receptors and complete neuroprotection.
3. For a negative control the cell medium was aspired and 525 μL
of 5 mM sodium azide in Hank’s Balanced Salt Solution
(HBSS) was added to the well, alternatively 100 mM acetic
acid can be used to achieve 100% cell death [26] (see Note 21).
4. Add in every well 25 μL of the compound as a 1 mM solution in
0.9% sterile saline.
5. For the NMDA concentration-response toxicity curve, NMDA
in concentrations from 1 μM to 1 mM was added to the wells.
6. Expose the cells to the compound by shaking at 37 C for 24 h.
7. Incubate the cells for 2 h with 0.5 mg mL1 MTT (see Note
22) at 37 C in a humidified atmosphere of 5% CO2.
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Dominik J. Essig et al.
8. Remove the medium gently and add 100 μL of DMSO to the
wells.
9. Shake the plate at 400 rpm for 15 min at 50 C to dissolve the
formazon product.
10. Analysis by ELISA microplate reader at 570 nm. The cell
viability is expressed in a percentage of A570 nm values of the
average of the sample vials to the control wells. These are
exposed to HBSS in the absence of any drug treatment as the
treatment of 5 mM sodium azide leads to complete cell death
(see step 3)
4
Notes
1. If the DP value is not close to the reference DP value in 0.5
difference, there is a strong possibility that the peptide/protein
is aggregating inside the cell, there is an air bubble trapped or
that there are some dust particles inside.
2. Wang resin is being used as it comes with an acid linker and it
can be bought preloaded with the first amino acid. As an
alternative, chlorotrityl resin can be easily used. This resin
provides an acid linker and also removes any possible side
chain racemization due to the steric bulk trityl groups.
3. The scale of the synthesis means the amount of resin that is
required to obtain a specific amount of crude peptide.
The amount of resin needed to achieve a determined scale is
determined by the loading of the resin usually provided as
mmol g1.
4. Here we just describe standard coupling reagents such as
HBTU; however, the reader should be aware that depending
on the difficulty of the synthesis, other more reactive coupling
reagents such as the phosphonium base salt PyAOP can
be used.
5. The Kaiser test usually just gives as an indication the blue color;
however, depending on the sequence and the amino acid, it is
possible to observe different bead colorations not stated in the
test. In such cases, we suggest to do a double coupling and if
there is no change on the color, continue working with
it. Alternatively, perform a test cleavage.
6. It is recommended to plan test cleavages along the synthesis
(specially for long sequences) as it will provide crucial information on which amino acids are more difficult or problematic to
couple. We strongly suggest to perform a test cleavage when
the peptide is completed as a mandatory step as it will provide
information on the peptide quality and in worst-case scenario,
if it is required to restart the synthesis.
PDZ Domain Peptide Inhibitors
175
7. It is recommended before cleavage to analyze the peptide
sequence and use an appropriate cleavage cocktail accordingly
to prevent unwanted side reactions while the cleavage step.
8. DMSO should be avoided with peptides that contains Cys,
Trp, or Met as they can easily undergo oxidation.
9. An alternative method for purifying a labeled peptide is using
desalting columns with an appropriate molecular weight cutoff
(MWCO). This will allow to remove the fluorophore, that has a
lower molecular weight, from the peptide without the need of a
HPLC purification.
10. Once the fluorophore conjugated peptide has been purified
and dissolved It is possible to determine its concentration
using the Lambert–Beer law knowing the extinction coefficient
and the absorption/emission wavelength of the fluorophore
(usually provided by the manufacturer) with a UV
spectrophotometer.
A
A ¼ ε l c ! c ¼ εl
:
where ε: molar absorptivity (L mol1 cm1), l: cuvette length
(cm), c concentration (mol L1), A: absorbance.
11. To synthesize an asymmetrical peptide on-resin, load the resin
with a 50/50 mixture of Fmoc-Asp(OtBu)-OH and FmocVal-OH on a 2-chlorotrityl chloride resin as the initial step
using diisopropylethylamine (DIPEA) (resin–amino acid–
DIPEA in 1:4:8) in DCM for 30 min followed by capping
with methanol (DCM–MeOH–DIPEA 17:2:1). Then follow
the conventional peptide synthesis explained Subheading 3.1
and the addition of PEG-diacid.
12. Linker length optimization is going to be crucial to optimize
the dimeric binder candidate for further studies. We recommend to test different PEGn diacids with different lengths and
screen for the best candidate.
13. The dilution curve should be planned so that the expected Kd
concentration is in the middle of the curve. If the binding
constant is unknown, it will need several optimization steps
to find the perfect range.
14. To prevent light quenching the fluorescent probe and to assure
its quality overtime we recommend covering it always with
aluminum foil and preparing all the solutions with the probe
in ice.
15. We strongly recommend filtrating all the buffers for ITC and if
possible the same for the peptide and protein solutions to have
accurate results.
16. The biotinylated peptides can be synthesized following the
standard solid-phase Fmoc synthesis procedure explained in
Subheading 3.1. The biotin can be coupled using the same
176
Dominik J. Essig et al.
procedure as with a standard amino acid. Biotin will be soluble
in DMF once DIPEA has been added to the solution.
17. Addition of a negative control, for example an inactive alanine
mutant.
18. For one mouse brain (m ¼ 0.4 g), use 4 mL of homogenization
buffer and do not move grinder above solvent level
19. As a negative control for the primary antibodies use protein
lysates from hippocampal tissue of previously described mutant
mice bearing a loss-of-function mutation in the Dlg1
(SAP-97), Dlg2 (PSD-93), Dlg3 (SAP-102), and Dlg4
(PSD-95) genes.
20. If samples contain serum or phenol red it can affect the readout intensity. Use serum or phenol free media if possible or use
a background control
21. Procaine (positive control) and procainamide (negative control) were investigated at 50 μM.
22. We recommend to store the MTT reagent at 4 C in the dark as
it is sensitive to light.
Acknowledgments
The chapter was supported by EU Horizon 2020 RIA under the
Marie Skłodowska-Curie grant agreement no. 675341
References
1. Wells JA, McClendon CL (2007) Reaching for
high-hanging fruit in drug discovery at protein–protein
interfaces.
Nature
450:1001–1009
2. De Las Rivas J, Fontanillo C (2010) Protein–protein interactions essentials: key concepts to
building and analyzing interactome networks.
PLoS Comput Biol 6:e1000807
3. Scott DE, Bayly AR, Abell C, Skidmore J
(2016) Small molecules, big targets: drug discovery faces the protein–protein interaction
challenge. Nat Rev Drug Discov 15:533–550
4. Sundell
GN,
Arnold
R,
Ali
M,
Naksukpaiboon P, Orts J, Güntert P, Chi CN,
Ivarsson Y (2018) Proteome-wide analysis of
phospho-regulated PDZ domain interactions.
Mol Syst Biol 14:e8129
5. Fanning AS, Anderson JM (1996) Protein–protein interactions: PDZ domain networks.
Curr Biol 6:1385–1388
6. Yu Y, Li S, Wang K, Wan X (2019) A PDZ
protein MDA-9/Syntenin: as a target for
cancer therapy. Comput Struct Biotechnol J
17:136–141
7. Zeng M, Shang Y, Guo T, He Q, Yung W-H,
Liu K, Zhang M (2016) A binding site outside
the canonical PDZ domain determines the specific interaction between Shank and SAPAP and
their function. Proc Natl Acad Sci U S A 113:
E3081–E3090
8. Münz M, Hein J, Biggin PC (2012) The role
of flexibility and conformational selection in
the binding promiscuity of PDZ domains.
PLoS Comput Biol 8:e1002749
9. Schreiber G, Keating AE (2011) Protein binding specificity versus promiscuity. Curr Opin
Struct Biol 21:50–61
10. Hill MD, Goyal M, Menon BK, Nogueira RG,
McTaggart RA, Demchuk AM et al (2020)
Efficacy and safety of nerinetide for the treatment of acute ischaemic stroke (ESCAPENA1): a multicentre, double-blind, randomised controlled trial. Lancet 395:878–887
PDZ Domain Peptide Inhibitors
11. Boissel J-P, Bros M, Schröck A, GödtelArmbrust U, Förstermann U (2004) Cyclic
AMP-mediated upregulation of the expression
of neuronal NO synthase in human A673 neuroepithelioma cells results in a decrease in the
level of bioactive NO production: analysis of
the signaling mechanisms that are involved.
Biochemistry 43:7197–7206
12. Schulz JB, Matthews RT, Klockgether T,
Dichgans J, Beal MF (1997) The role of mitochondrial dysfunction and neuronal nitric
oxide in animal models of neurodegenerative
diseases. Mol Cell Biochem 174:193–197
13. Pou S, Keaton L, Surichamorn W, Rosen GM
(1999) Mechanism of superoxide generation
by neuronal nitric-oxide synthase. J Biol
Chem 274:9573–9580
14. Grotta J, Clark W, Coull B, Pettigrew LC,
Mackay B, Goldstein LB, Meissner I,
Murphy D, LaRue L (1995) Safety and tolerability of the glutamate antagonist CGS 19755
(selfotel) in patients with acute ischemic stroke:
results of a phase IIa randomized trial. Stroke
26:602–605
15. Sveinbjornsdottir S, Sander J, Upton D,
Thompson P, Patsalos P, Hirt D, Emre M,
Lowe D, Duncan JS (1993) The excitatory
amino acid antagonist D-CPP-ene (SDZ
EAA-494) in patients with epilepsy. Epilepsy
Res 16:165–174
16. Albers G, Atkinson R, Kelley R, Rosenbaum D
(1995) Safety, tolerability, and pharmacokinetics of the N-methyl-D-aspartate antagonist
dextrorphan in patients with acute stroke.
Stroke 26:254–258
17. Albers GW, Goldstein LB, Hall D, Lesko LM,
Investigators AAS (2001) Aptiganel hydrochloride in acute ischemic stroke: a randomized
controlled trial. JAMA 286:2673–2682
177
18. Hoyte L, Barber P, Buchan A, Hill M (2004)
The rise and fall of NMDA antagonists for
ischemic stroke. Curr Mol Med 4:131–136
19. De Keyser J, Sulter G, Luiten PG (1999) Clinical trials with neuroprotective drugs in acute
ischaemic stroke: are we doing the right thing?
Trends Neurosci 22:535–540
20. Aarts M, Liu Y, Liu L, Besshoh S, Arundine M,
Gurd JW, Wang YT, Salter MW, Tymianski M
(2002) Treatment of ischemic brain damage by
perturbing NMDA receptor-PSD-95 protein
interactions. Science 298:846–850
21. Nourry C, Grant SG, Borg J-P (2003) PDZ
domain proteins: plug and play! Sci STKE
2003:re7
22. Chi CN, Bach A, Strømgaard K, Gianni S,
Jemth P (2012) Ligand binding by PDZ
domains. Biofactors 38:338–348
23. Nevola L, Giralt E (2015) Modulating protein–protein interactions: the potential of peptides. Chem Commun (Camb) 51:3302–3315
24. Bach A, Clausen BH, Møller M, Vestergaard B,
Chi CN, Round A et al (2012) A high-affinity,
dimeric inhibitor of PSD-95 bivalently interacts with PDZ1-2 and protects against ischemic brain damage. Proc Natl Acad Sci U S A
109:3317–3322
25. Sommer JB, Bach A, Malá H, Strømgaard K,
Mogensen J, Pickering DS (2017) In vitro and
in vivo effects of a novel dimeric inhibitor of
PSD-95 on excitotoxicity and functional recovery after experimental traumatic brain injury.
Eur J Neurosci 45:238–248
26. Jensen J, Schousboe A, Pickering D (1998)
AMPA receptor mediated excitotoxicity in neocortical neurons is developmentally regulated
and dependent upon receptor desensitization.
Neurochem Int 32:505–513
Chapter 11
Dynamic Control of Signaling by Phosphorylation of PDZ
Binding Motifs
Márton A. Simon and László Nyitray
Abstract
The dynamic regulation of protein–protein interactions (PPIs) involves phosphorylation of short liner
motifs in disordered protein regions modulating binding affinities. The ribosomal-S6-kinase 1 is capable of
binding to scaffold proteins containing PDZ domains through a PDZ-binding motif (PBM) located at the
disordered C-terminus of the kinase. Phosphorylation of the PBM dramatically changes the interactome of
RSK1 with PDZ domains exerting a fine-tuning mechanism to regulate PPIs. Here we present in detail
highly effective biophysical (fluorescence polarization, isothermal calorimetry) and cellular (proteinfragment complementation) methods to study the effect of phosphorylation on RSK1-PDZ interactions
that can be also applied to investigate phosphoregulation of other PPIs in signaling pathways.
Key words PDZ domain, Fluorescence polarization, Isothermal titration calorimetry, Bimolecular
fragment complementation assay, NanoBiT, Protein–protein interaction
1
Introduction
Dynamic regulation of protein–protein interactions (PPIs) is essential for maintaining cellular physiology [1, 2]. One of the key
mechanisms for such regulation, reversible phosphorylation, frequently occurs in disordered protein regions and can alter interactions with structured domains, controlling the functional network
of the proteome [3–5]. An example of this scenario, the globular
PDZ (PSD95/DLG1/ZO-1) domains interact with disordered
binding regions, called PDZ-binding motifs (PBMs) that are usually located at the disordered C-terminus of their targets [6–8]. The
interactions between PDZ domains and their partners containing
the PBM are affected by phosphorylation events, exerting a
dynamic control of signaling pathways [9, 10].
Phosphorylation is usually considered as a binary chemical
switch between the phosphorylated and unphosphorylated states,
enabling or disabling PPIs thus resulting in drastic changes in
binding affinities [11]. However, controlling signaling pathways
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_11, © Springer Science+Business Media, LLC, part of Springer Nature 2021
179
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Márton A. Simon and László Nyitray
often requires fine-tuning, resulting in minor alterations in thermodynamics and kinetics to regulate cellular processes more effectively
[3, 12]. Our recent study provided evidence that in the PDZ
interactome of RSK1, phosphorylation does not usually act as
hard switch, but its effect rather can be described as a fine-tuning
regulatory mechanism (dimming) [13, 14].
Although phosphorylation events usually involve drastic
changes in the interactome, PDZ interactions are mostly considered weak, in the μM range, therefore the effects triggered by
phosphorylation are subtle. To measure such small effects on
weak interactions with high precision, robust and sensitive methods
are required. Thus, these interactions should be characterized
either by robust high-throughput (HTP) methods such as Holdup
assay [15], surface plasmon resonance [16], micro-scale thermophoresis [10] and fluorescence polarization (FP) [14]; or with
accurate low-throughput structural/biophysical methods such as
isothermal titration calorimetry (ITC) [9], nuclear magnetic resonance spectroscopy [17], X-ray diffraction [9]; or to be closer to
the in vivo system, with cell-based approaches such as coimmunoprecipitation [18], proximity ligation assay [19], or proteinfragment complementation assay (PCA) [14].
Here we present a detailed workflow to investigate the impact
of phosphorylation on RSK1-PDZ domain interactions as a proof
of the concept for studying phosphorylation regulation of signaling
pathways. This chapter covers in vitro biochemical and biophysical
methods using isolated PDZ domains and RSK1 peptides as well as
a cell-based approach using full length PDZ domains and RSK1 to
extensively characterize the effect of phosphorylation of PBMs on
the PDZ interactome. We chose to describe in detail FP assays since
it is a high-throughput, simple, sensitive, and cost-effective method
to obtain dissociation constants of PPIs. ITC, a robust biophysical
method, which provides more thermodynamic data, and a novel
cell-based protein-fragment complementation assay (NanoBit)
were chosen mainly because they are effective validation methods
for HTP data acquired by FP measurements. The scheme of RSK1
and their peptides used for the experiments is shown in Fig. 1.
2
Materials
2.1 In Vitro
Phosphorylation
of RSK1683–735
1. Phosphorylation buffer (buffer A): 150 mM NaCl, 5 mM
MgCl2, 100 μM TCEP, 20 mM HEPES–NaOH pH 7.5. Dissolve 0.88 g NaCl, 0.48 g HEPES, and 48 mg MgCl2 in 95 ml
Milli-Q water. Add 20 μl 0.5 M TCEP solution, then fill the
volume to 100 ml and adjust the pH to 7.5 with sodium
hydroxide solution. Store at 20 C in 10 ml aliquots.
Regulation of RSK1-PDZ Domain Interactions
181
Fig. 1 The scheme of RSK1 and their peptides used for the experiments. (a) RSK1 is a tandem kinase
consisting of an AGC-type N-terminal kinase domain (NTKD) and a CAMK-type C-terminal kinase domain
(CTKD). Upon phosphorylation by ERK2 at T573 in the C-terminal tail (CTT), the CTKD autophosphorylates S382
in the linker region, which is followed by the recruitment of PDK1 and the phosphorylation of the NTKD [9]. (b)
The CTT contains some overlapping linear motifs including an autoinhibitory segment and the ERK docking
motif. At the very end, the PBM is found capable of binding to PDZ domains [9]. (c) The canonical sequence
(hydrophobic L/V in position 0) belongs to class 1 PBMs containing S/T in position 2. Interestingly, upon
RSK1 activation after the phosphorylation of T573 by ERK2, the CTKD autophosphorylates positions (indicated
by red color) S732 (major phosphosite) and T733 and T734 (minor phosphosites) [9]
2. 100 mM ATP stock solution. Dissolve 10 mg ATP disodiumsalt hydrate in 180 μl Milli-Q water. Store at 20 C in 60 μl
aliquots.
3. RSK1-T573E411–735 is a constitutively active T573E mutant
version of the C-terminal kinase domain (N411-L735) of
RSK1. Pure RSK1-T573E411–735 can be obtained through a
tandem purification approach using GST- and Ni2+-affinity
chromatography sequential steps as described elsewhere
[14]. Upon mixing with 20% glycerol, 100 μM TCEP (reducing agent) and adjusting its final concentration to 50 μM, the
recombinant RSK1-T573E411–735 protein is distributed in
500 μl aliquots that are subsequently frozen in liquid nitrogen
and stored at 80 C.
4. 1 mM RSK1683–735 stock solution. The C-terminal peptide of
RSK1 (RSK1683–735) can either be chemically synthesized or
produced as a recombinantly expressed fragment with an
N-terminal cleavable GST tag. To prepare a 1 mM stock solution of RSK1683–735, dissolve approximately 7.2 mg lyophilized
peptide in 1 ml phosphorylation buffer (buffer A). Adjust the
pH to 7.5. Store at 20 C in 500 μl aliquots [14].
5. Eluent A: 0.1% (v/v) trifluoroacetic acid (TFA) solution. Add
1 l Milli-Q water into a graduated cylinder then supplement it
with 1 ml TFA.
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Márton A. Simon and László Nyitray
6. Eluent B: 0.09% (v/v) TFA in acetonitrile. Add 500 ml HPLC
grade acetonitrile into a graduated cylinder then supplement it
with 450 μl TFA.
7. Jupiter 300 C5 RP-HPLC column (Phenomenex).
8. 0.45 μm pore size syringe filters.
2.2 Fluorescence
Polarization (FP)
1. FP buffer (buffer B): 150 mM NaCl, 100 μM TCEP, 0.01%
Tween 20, 40 mM HEPES–NaOH pH 7.5. Dissolve 0.88 g
NaCl and 0.95 g HEPES in 95 ml Milli-Q water. Add 20 μl
0.5 M TCEP solution and 100 μl 10% Tween 20. Adjust the
pH to 7.5 with sodium hydroxide solution, then fill it to
100 ml with Milli-Q water. Store at 20 C in 10 ml aliquots.
2. 10 μM labeled peptides: Prepare 1 mM stock solutions of the
chemically synthesized and carboxyfluorescein-labeled phospho/unphospho RSK1729–735 peptide by dissolving approximately 0.7 mg phospho/unphospho peptide in 0.5 ml DMSO.
Determine the concentration of the stock solutions by measuring the absorbance of the 5(6)-carboxyfluorescein at 495 nm
(εcoeff ¼ 82,000 M1 cm1). Dilute the stock solutions in
buffer B to 10 μM. Store at 20 C in 100 μl aliquots.
3. 1 mM unlabeled peptide stock solution: To prepare 1 mM
stock
solutions
of
unlabeled
phospho/unphospho
RSK1683–735, dissolve approximately 7.2 mg of lyophilized
peptides in 1 ml Milli-Q water. Determine the concentration
by measuring the absorbance of the Tyr residue at 280 nm
(εcoeff ¼ 1490 M1 cm1). Store at 20 C in 100 μl aliquots.
4. PDZ domains are kept as stock solutions of at least 1 mM. Pure
PDZ domains can be obtained through a tandem purification
approach using Ni2+ and MBP affinity chromatography after
bacterial expression described elsewhere [9]. Upon mixing
with 20% glycerol, 100 μM TCEP (reducing agent) and adjusting its final concentration to 1 mM, the recombinant PDZ
domains are distributed in 100 μl aliquots that are subsequently
frozen in liquid nitrogen and stored at 80 C.
5. Black 384-well nonbinding microplates.
6. Plate reader.
2.3 Isothermal
Titration
Calorimetry (ITC)
1. ITC buffer (Buffer C): 150 mM NaCl, 50 nM TCEP, 20 mM
HEPES–NaOH pH 7.5. Dissolve 4.76 g HEPES and 8.77 g
NaCl in 950 ml deionized water. Add 100 μl 0.5 M TCEP and
adjust the pH to 7.5 with sodium hydroxide solution, then
complete the volume to 1 l with Milli-Q water.
2. 1 mM PDZ domain stock solution. Described above previously
in item 4 Subheading 2.2.
Regulation of RSK1-PDZ Domain Interactions
183
3. Peptides: use the stocks, which has been described previously.
4. Instruments used: VP-ITC, ITC200, or PEAQITC.
2.4 Protein Fragment
Complementation
Assay (NanoBiT)
1. NanoBiT PPI MCS starter system (Promega). Full length
RSK1 is cloned into the N-terminal-tagged SmBiT vector
(pBiT2.1-N[TK/SmBiT]), and the PDZ domain containing
interaction partners are cloned into the N-terminal-tagged
LgBiT vector (pBiT1.1-N[TK/LgBiT]).
2. HEK293T cells.
3. Dulbecco’s modified Eagle’s medium (DMEM) supplemented
with 10% fetal bovine serum and 1% penicillin/streptomycin/
amphotericin B. To prepare 250 ml, add 25 ml fetal bovine
serum and 2.5 ml penicillin/streptomycin/amphotericin B to
222.5 ml DMEM. Store at 4 C in 50 ml aliquots.
4. Phosphate buffered saline (PBS) buffer.
5. FuGene HD reagent (Promega).
6. Opti-MEM I buffer (Thermofisher).
7. CO2-independent medium supplemented with 2 mM L-glutamine and 1% antibiotics. To prepare 100 ml stock solution, add
1 ml penicillin/streptomycin/amphotericin B and 1 ml
200 mM L-glutamine to 98 ml CO2-independent medium.
Store at 4 C in 50 ml aliquots.
8. 135 μg/ml Epidermal growth factor (EGF): to prepare a stock
solution of 135 μg/ml, add 1 mg hEGF to 7.4 ml Milli-Q
sterile water. Store at 20 C in 0.5 ml aliquots.
9. Nano-Glo reagent (Promega).
10. White TC-treated 96-well plates.
11. Plate reader.
3
Methods
3.1 In Vitro
Phosphorylation
of RSK1683–735
1. Add 50 μl of ATP and 500 μl of RSK1683–735 substrate to
3.95 ml phosphorylation buffer (buffer A) and vortex it gently,
then supplement with 500 μl of RSK1-T573E411–735 kinase
and mix it carefully (see Note 1).
2. Incubate the reaction mixture in a shaker at 37 C for 3 h.
3. Proceed with the purification of the reaction mixture by
RP-HPLC.
4. Equilibrate the RP-HPLC column with Eluent A for approximately 5 min.
5. Inject the filtered reaction mixture onto the column.
6. Wash thoroughly with Eluent A until the baseline stabilizes.
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Márton A. Simon and László Nyitray
7. Elute the peptide by a linear gradient of Eluent B (see Note 2).
8. Freeze the fractions at 80 C, then lyophilize them overnight.
9. Check the molecular mass of the relevant fraction by mass
spectrometry (important, see Note 3). Prepare the stock solution for FP as described above and store at 20 C.
3.2 Fluorescence
Polarization
(FP) to Study
the Effects
of Phosphorylation
Fluorescence polarization (FP) is a fast and high-throughput
method that is capable of measure steady-state dissociation constants of PPIs [20]. FP measurements can be performed in a direct
and competitive way (Fig. 2). In a direct assay, the formation of the
labeled (phospho/unphospho) peptide–PDZ domain complex is
monitored as a function of the PDZ domain concentration. In a
competitive assay, the dissociation of the labeled peptide–PDZ
domain complex is investigated as a function of the unlabeled
PDZ partner concentration [21]. The advantage of the former is
its simplicity; however, the fluorescent moiety can affect the affinity
of the peptide as the competitive assay benefits from the binding of
the competitors are not biased by the fluorescent moiety
[22]. Phosphorylation patterns can be studied by FP method in a
high-throughput way, quickly providing reliable steady-state dissociation constant for the PDZ–phospho/unphosphoPBM interaction by applying only small amounts of PDZ/PBM
compounds [14].
3.2.1 Direct Assay
1. Prepare 500 μl of tracer solution #1 (labeled phospho/unphospho RSK1-PBM729–735) by adding 2.5 μl of labeled peptide
solution to 497.5 μl of buffer B to reach a 50 nM final
concentration.
2. Prepare 100 μl of tracer solution #2 (labeled phospho/unphospho RSK1-PBM729–735) by adding 5 μl of labeled peptide
solution to 495 μl of buffer B to reach a 100 nM final
concentration.
3. Distribute 50 μl of tracer solution #2 into the first well and
50 μl of tracer solution #1 in the next seven vials in a 96-well
mixing plate (see Note 4).
4. Dilute the PDZ stock solutions in buffer B to 200 μM final
concentration by mixing 20 μl PDZ stock solution with 80 μl
buffer B (assuming 1 mM PDZ stock solutions).
5. Mix 50 μl of the PDZ domain solution with tracer solution #2
in the first well to get 100 μM final concentration (see Note 4).
6. Perform a twofold serial dilution in the following way: pipet
50 μl solution from the very first well to the next well, mix it
carefully, then pipet 50 μl from this one to the next well. Repeat
this, until you reach the one, prior to the last well. Do not
dilute the last well.
Regulation of RSK1-PDZ Domain Interactions
185
Fig. 2 Fluorescence polarization assays. (a) In the direct assay, the fast-rotating fluorescence-labeled peptide
(fRSK1729–735) is added to increasing amount of target protein (MAST2) and excited by polarized light. The
smaller depolarization of the emitted light compared to the polarization of the free tracer indicates the binding,
reporting the association of the PDZ domain-tracer complex. (b) In the competitive assay, the unlabeled
peptide (pRSK1729–735) is added in different concentrations to the previously formed PDZ domain-tracer
complex (MAST2-fRSK1729–735), reaching approximately 70% saturation by 10 μM PDZ domain (MAST2) in
final concentration. The increasing amount of the unlabeled ligand compete with the labeled tracer
(fRSK1729–735) resulting in free labeled peptide indicated by the larger depolarization of the emitted light
compared to the initial value
7. Pipet 15–15 μl from the last well (which is not diluted) of the
serial dilution to three horizontal wells of the black 384-well
plate, then repeat this procedure with all wells of the serial
dilution. This way, 3 8 (24) wells’ fluorescence is measured
by the plate reader.
8. Analyze the data using Origin software (or other data analyzer
software package) applying a quadratic binding equation (see
Note 5).
3.2.2 Competitive Assay
For a competitive assay, in which the unlabeled RSK fragment
competes with the labeled peptide fragment for the binding site
of the PDZ domain, one should calculate the amount of PDZ
domain added to the labeled peptide, as this time the PDZ domain
is supplemented in the tracer solutions. As a rule of thumb, the
saturation of the binding site of the PDZ domain with the labeled
peptide should be 60–80%. By fixing the concentration of the
186
Márton A. Simon and László Nyitray
labeled peptide to 50 nM, the amount of PDZ domain can be
calculated based on the direct FP measurement.
1. Prepare 500 μl of tracer solution 3# (labeled phospho/unphospho RSK1-PBM729–735 and PDZ domain) by adding 2.5 μl of
labeled peptide solution and VPDZ μl to 497.5 - VPDZ μl of
buffer B to reach a 50 nM final concentration for the labeled
peptide which saturates the PDZ domain in 60–80%.
2. Prepare 100 μl of tracer solution 4# (labeled phospho/unphospho RSK1-PBM729–735 and PDZ domain) by adding 5 μl of
labeled peptide solution and 2VPDZ μl to 495 - 2VPDZ μl of
buffer B to reach a 100 nM final concentration for the labeled
peptide which saturates the PDZ domain in 60–80%.
3. Distribute 50 μl of tracer solution #4 into the first well and
50 μl of tracer solution #3 in the next seven vials in a 98-well
mixing plate (see Note 4).
4. Dilute the unlabeled RSK fragment stock solutions in buffer B
to 200 μM final concentration by mixing 20 μl RSK stock
solution/RSK recombinant fragment stock solution with
80 μl buffer B (assuming 1 mM unlabeled recombinant fragment stock solutions).
5. Mix 50 μl of the unlabeled recombinant fragment solution with
tracer solution #4 in the first well to get 100 μM final concentration (see Note 4).
6. Perform a twofold serial dilution in the following way: pipet
50 μl solution from the very first well to the next well, mix it
carefully, then pipet 50 μl from this one to the next well. Repeat
this, until you reach the one, prior to the last well. Do not
dilute the last well.
7. Pipet 15–15 μl from the last well (which is not diluted) of the
serial dilution to three horizontal wells of the black 384-well
plate, then repeat this procedure with all wells of the serial
dilution. This way, 3 8 (24) wells’ fluorescence is measured
by the plate reader.
8. Analyze the data using Origin software (or other data analyzer
software package) applying competitive binding models [23]
(see Note 5).
3.3 Isothermal
Titration Calorimetry
(ITC) to Study
the Effects
of Phosphorylation
Besides validating steady-state Kd values, ITC measurements provide additional thermodynamic parameters of the reaction (such as
the change in enthalpy or entropy), moreover it is a label-free
method to detect interactions [24]. As drawbacks, ITC measurements are low-throughput and require large amount of material
(compared to FP assays). Moreover very weak interactions cannot
be characterized reliably. Though PDZ domain–RSK1 interactions
fall into the higher micromolar range, the phosphorylation induced
Regulation of RSK1-PDZ Domain Interactions
changes in binding affinity can be monitored by
measurements [9].
187
ITC
1. Add 0.2 ml of PDZ stock solution to 1.8 ml Buffer C. Dialyze
against Buffer C together with the RSK recombinant fragment
stock solution at 4 C overnight.
2. Determine the PDZ and the unlabeled RSK recombinant fragment concentrations (see Note 7). Alternatively, the lyophilized
peptide can be dissolved in buffer C.
3. Fill the ITC cell with the PDZ domain solution (see Note 9).
4. Fill the ITC syringe with the RSK recombinant fragment solution (see Note 9).
5. Wait for the equilibration of temperature between the reference and sample cells (until the difference power known as DP
reach close to zero), then run the ITC program (see Note 10).
6. Analyze data using the built-in model “one set of sites” in
Origin for ITC software. For more information, check the
following reference [9].
3.4 NanoBiT Protein–
Protein Interaction
Assay to Study
the Effects
of Phosphorylation
NanoBiT is a split-luciferase fragment complementation assay
(Fig. 3), which is appropriate for monitoring dynamic changes in
PPIs [25]. Instead of using isolated fragments and peptides of PDZ
domain and PBM containing proteins, the interaction can be studied with full length proteins in a cellular environment. The advantage of the NanoBiT system over other bimolecular fragment
complementation assays is the low, almost endogenous protein
expression due to the HSV promoter and the reversible interaction
between the split NanoLuc fragment that makes possible to monitor dynamic changes in signaling pathways beside the steady-state
signal [9]. The association and dissociation of the PDZ–RSK1
complexes can be investigated under EGF stimulation conditions,
which allows one to characterize the effect of phosphorylation on
PBMs.
1. Plate approximately 20,000 HEK293T cells/well in a white
TC-treated 96-well plate by distributing 100 μl of a 200,000
cell/ml cell suspension in six parallel wells per measurement
(see Note 11). For measurements at 37 C, add 100 μl sterile
PBS to the outside wells and spaces between the inner wells.
2. Incubate the plate at 37 C with 5% CO2 for 16–24 h.
3. Dilute the RSK1-SmBiT and PDZ-LgBiT plasmid DNAs to
150 ng/μl.
4. 4. Pipet 52.44 μl of Opti-MEM I buffer to a sterile Eppendorf
tube per measurement, add 2.52 μl of each of the SmBiT and
LgBiT plasmids, then add 2.52 μl FuGene HD reagent and mix
it by pipetting (see Note 12).
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Márton A. Simon and László Nyitray
Fig. 3 The NanoBiT PPI assay. The proteins of interest are fused either with LgBiT
or SmBiT tag. Upon interaction, the two fragments of the NanoLuc luciferase
complete each other producing luminescence by adding a furimazine substrate
(Nano-Glo). As the interaction between the fragments is reversible, dynamic
signal can be monitored beside the steady-state measurement
5. Incubate the transfection mixture on room temperature for at
least 10 min.
6. Add 8 μl of transfection mixture to each well and incubate the
plate at 37 C, 5% CO2 for 4–6 h.
7. To monitor signal transduction, remove carefully the medium
from each well and add 100 μl of serum-free DMEM for
washing out FBS (see Note 13).
8. Remove the washing medium and add 100 μl of CO2 independent medium (supplemented with L-glutamine and
antibiotics).
9. Incubate the plate at 37 C with 5% CO2 for further 18–20 h
(see Note 14).
10. Set the plate reader to 37 C (optional).
11. Prepare the Nano-Glo Live Cell reagent as follows: Melt the
Nano-Glo LCS Dilution buffer and the Nano-Glo Live Cell
Substrate on ice. For 6 wells, add 8.75 μl Nano-Glo Live Cell
Substrate to 166.25 μl Nano-Glo LCS Dilution buffer, mix it
gently by pipetting (see Note 15).
12. Add 25 μl of Nano-Glo Live Cell reagent to each well, then
start measuring the luminescence with 1 min interval. Record
the baseline until luminescence begins to decrease (see Note
16).
Regulation of RSK1-PDZ Domain Interactions
189
13. Make a 100-fold dilution of the EGF stock in Milli-Q water to
obtain a 1.35 μg/ml EGF solution.
14. Stimulate the cells of 3 wells with 10 μl of 1.35 μg/ml EGF
solution (100 ng/ml final concentration of EGF) while treating the cells of 3 control wells with Milli-Q water (see Note
17).
15. Start the measurement for 1 h recording the luminescence
signal for each well as many times as possible.
16. Evaluate the data by normalizing the signal. For a steady state
measurement, values are normalized to the luminescence of the
wild-type protein. For a dynamic measurement, the values are
normalized to the average signal decay of the luminescence of
unstimulated wells.
4
Notes
1. The kinase of interest is susceptible to precipitation as the pH
has impact on the stability of the kinase, therefore do not forget
to adjust the pH of the reaction mixture to 7.5 before adding
the kinase itself.
2. Peptides typically elute between 30% and 45% Eluent B.
3. Note that the phosphorylation reaction sometimes does not go
very well due to the characteristics of the applied kinase. Do not
forget to check your freshly prepared fraction by mass
spectrometry!
4. As a result, the concentration of the labeled peptide (and the
PDZ domain in competitive measurements) will be equal in all
wells. If well-concentrated stocks solution of PDZ domain and
unlabeled recombinant fragment are available (e.g., 5 mM),
then One can add the PDZ to tracer solution #1 (e.g., 5 μl
5 mM PDZ + 95 μl tracer solution #1 followed by the twofold
dilution step) in direct titration and the unlabeled RSK fragment to tracer solution #3 (e.g., 5 μl 5 mM unlabeled
RSK + 95 μl tracer solution #3 followed by the twofold dilution
step) in competitive titration.
5. An automated, unbiased fitting program called ProFit is available, which can be found on GitHub (https://github.com/
GoglG/ProFit) and is described in reference [26].
6. For the competition assay, one can apply less saturated complexes in cases of weaker interactions if the difference between
the base polarization and the polarization at the given saturation is large enough (>50 mP).
7. For a competitive assay, a direct FP measurement is also
required to be analyzed. Furthermore, to avoid experimental
190
Márton A. Simon and László Nyitray
artifacts, the deviation between the experimental windows of
the direct and competitive measurements should not
exceed 20%.
8. During dialysis samples are diluted to some extent. Never use
DTT as a reducing agent during ITC measurement. The
amount of samples needed for the measurements depends on
the type of instrument.
9. Be aware of avoiding air bubbles in the sample cell and in the
syringe. Note that larger volumes of samples are needed to fill
the sample cell and the syringe than the actual volumes of the
cell and syringe.
10. If the change of heat is small, measure the interaction of
interest on a different temperature, as ΔH is temperaturedependent.
11. You should use only the inner 60 cells (minimizing the potential for thermal gradient and also for evaporation at the edges).
At least 4–5 parallel measurements is recommended for individual interactions.
12. Pipet FuGene HD reagent directly to the solution, not to the
plastic wall of the tube.
13. Regarding the small cell number (20,000/well), try to avoid to
disturbing the cell monolayer. Remove the medium slowly
with extra care to maintain it.
14. For higher expression level of the protein(s) of interest, incubate the cells for 36–42 h before the measurement.
15. Always prepare Nano-Glo Live Cell reagent freshly. The furimazine component will decay to some extent. Do not freezethaw the Nano-Glo Live Cell Substrate more than 4–5 times.
16. The luminescence signal first increases, reaches a plateau, then
begins to decrease. This usually takes 15–30 min, depending
on the number of freeze–thaw cycles.
17. Always add water prior to EGF (or any other compound). Be as
fast as possible!
Acknowledgments
We thank Dr. Gergő Gógl for reading the manuscript. We also
thank Viktória Bilics for contributing in the FP measurement.
This work was supported by the National Research, Development
and Innovation Office (NKFIH) grants K119359 (to LN). MAS
was supported through the New National Excellence Program of
the Hungarian Ministry of Human Capacities. Project no. 20181.2.1-NKP-2018-00005 has been implemented with the support
provided from the National Research, Development and
Regulation of RSK1-PDZ Domain Interactions
191
Innovation Fund of Hungary, financed under the 2018-1.2.1-NKP
funding scheme. This work was completed in the ELTE Thematic
Excellence Programm 2020 Supperted by the Nation Research,
Development and Innovation Office (TKP2020-IKA-05)
References
1. Loregian A, Palù G (2005) Disruption of
protein-protein interactions: towards new targets for chemotherapy. J Cell Physiol
204:750–762. https://doi.org/10.1002/jcp.
20356
2. Vidal M, Cusick ME, Barabási AL (2011)
Interactome networks and human disease.
Cell
144:986–998.
https://doi.org/10.
1016/j.cell.2011.02.016
3. Nishi H, Hashimoto K, Panchenko AR (2011)
Phosphorylation in protein-protein binding:
effect on stability and function. Structure
19:1807–1815. https://doi.org/10.1016/j.
str.2011.09.021
4. Landry CR, Freschi L, Zarin T, Moses AM
(2014) Turnover of protein phosphorylation
evolving under stabilizing selection. Front
Genet 5:1–6. https://doi.org/10.3389/
fgene.2014.00245
5. Van Roey K, Uyar B, Weatheritt RJ, Dinkel H,
Seiler M, Budd A, Gibson TJ, Davey NE
(2014) Short linear motifs: ubiquitous and
functionally diverse protein interaction modules directing cell regulation. Chem Rev
114:6733–6778. https://doi.org/10.1021/
cr400585q
6. Lee HJ, Zheng JJ (2010) PDZ domains and
their binding partners: structure, specificity,
and modification. Cell Commun Signal
8:1–18.
https://doi.org/10.1186/1478811X-8-8
7. Luck K, Charbonnier S, Travé G (2012) The
emerging contribution of sequence context to
the specificity of protein interactions mediated
by PDZ domains. FEBS Lett 586:2648–2661.
https://doi.org/10.1016/j.febslet.2012.03.
056
8. Nourry C, Grant SGN, Borg JP (2003) PDZ
domain proteins: plug and play! Sci STKE
2003:1–13
9. Gógl G, Biri-Kovács B, Póti ÁL, Vadászi H,
Szeder B, Bodor A, Schlosser G, Ács A,
Turiák L, Buday L, Alexa A, Nyitray L, Reményi A (2018) Dynamic control of RSK complexes by phosphoswitch-based regulation.
FEBS J 285:46–71. https://doi.org/10.
1111/febs.14311
10. Sundell
GN,
Arnold
R,
Ali
M,
Naksukpaiboon P, Orts J, Güntert P, Chi CN,
Ivarsson Y (2018) Proteome-wide analysis of
phosphor-regulated PDZ domain interactions.
Mol Syst Biol 14:1–22. https://doi.org/10.
15252/msb.20178129
11. Nishi H, Shaytan A, Panchenko AR (2014)
Physicochemical mechanisms of protein regulation by phosphorylation. Front Genet
5:1–10.
https://doi.org/10.3389/fgene.
2014.00270
12. Pawson T (2004) Specificity in signal transduction: from phosphotyrosine-SH2 domain
interactions to complex cellular systems. Cell
116:191–203.
https://doi.org/10.1016/
S0092-8674(03)01077-8
13. Pedersen SW, Albertsen L, Moran GE,
Levesque B, Pedersen SB, Bartels L,
Wapenaar H, Ye F, Zhang M, Bowen ME,
Strømgaard K (2017) Site-specific phosphorylation of PSD-95 PDZ domains reveals finetuned regulation of protein-protein interactions. ACS Chem Biol 12:2313–2323.
https://doi.org/10.1021/acschembio.
7b00361
14. Gógl G, Biri-Kovács B, Durbesson F, Jane P,
Nomine Y, Kostmann C, Bilics V, Simon M,
Reményi A, Vincentelli R, Trave G, Nyitray L
(2019) Rewiring of RSK–PDZ interactome by
linear motif phosphorylation. J Mol Biol
431:1234–1249. https://doi.org/10.1016/j.
jmb.2019.01.038
15. Vincentelli R, Luck K, Poirson J et al (2015)
Quantifying domain-ligand affinities and specificities by high-throughput holdup assay. Nat
Methods 12:787–793. https://doi.org/10.
1038/nmeth.3438
16. Mortier E, Wuytens G, Leenaerts I, Hannes F,
Heung MY, Degeest G, David G, Zimmermann P (2005) Nuclear speckles and nucleoli
targeting by PIP2-PDZ domain interactions.
EMBO J 24:2556–2565. https://doi.org/10.
1038/sj.emboj.7600722
17. Gianni S, Walma T, Arcovito A, Calosci N,
Bellelli A, Engström A, Travaglini-Allocatelli C,
Brunori M, Jemth P, Vuister GW (2006) Demonstration of long-range interactions in a PDZ
domain by NMR, kinetics, and protein engineering. Structure 14:1801–1809. https://
doi.org/10.1016/j.str.2006.10.010
192
Márton A. Simon and László Nyitray
18. Thomas GM, Rumbaugh GR, Harrar DB,
Huganir RL (2005) Ribosomal S6 kinase
2 interacts with and phosphorylates PDZ
domain-containing proteins and regulates
AMPA receptor transmission. Proc Natl Acad
Sci U S A 102:15006–15011. https://doi.
org/10.1073/pnas.0507476102
19. Shi GX, Yang WS, Jin L et al (2017) RSK2
drives cell motility by serine phosphorylation
of LARG and activation of rho GTPases. Proc
Natl Acad Sci U S A 115:E190–E199. https://
doi.org/10.1073/pnas.1708584115
20. Park S-H, Raines RT (2015) Fluorescence
polarization assay to quantify protein-protein
interactions.
Methods
Mol
Biol
261:161–165. https://doi.org/10.1385/159259-762-9:161
21. Roehrl MHA, Wang JY, Wagner G (2004)
Discovery of small-molecule inhibitors of the
NFAT-calcineurin interaction by competitive
high-throughput fluorescence polarization
screening. Biochemistry 43:16067–16075.
https://doi.org/10.1021/bi048232o
22. Hall MD, Yasgar A, Peryea T et al (2016)
Fluorescence polarization assays in highthroughput screening and drug discovery: a
review. Methods Appl Fluoresc 4:022001.
https://doi.org/10.1088/2050-6120/4/2/
022001
23. Roehrl MHA, Wang JY, Wagner G (2004) A
general framework for development and data
analysis of competitive high-throughput
screens for small-molecule inhibitors of
protein-protein interactions by fluorescence
polarization. Biochemistry 43:16056–16066.
https://doi.org/10.1021/bi048233g
24. Macek P, Cliff MJ, Embrey KJ et al (2018) Myc
phosphorylation in its basic helix-loop-helix
region destabilizes transient-helical structures,
disrupting max and DNA binding. J Biol Chem
293:9301–9310. https://doi.org/10.1074/
jbc.RA118.002709
25. Dixon AS, Schwinn MK, Hall MP et al (2016)
NanoLuc complementation reporter optimized for accurate measurement of protein
interactions in cells. ACS Chem Biol
11:400–408.
https://doi.org/10.1021/
acschembio.5b00753
26. Simon M, Gógl G, Ecsédi P, et al (2019) High
throughput competitive fluorescence polarization assay reveals functional redundancy in the
S100 protein family. bioRxiv. https://doi.org/
10.1101/718155
Chapter 12
Chemical Synthesis of PDZ Domains
Christin Kossmann, Sana Ma, Louise S. Clemmensen,
and Kristian Strømgaard
Abstract
Developments in chemical protein synthesis have enabled the generation of tailor-made proteins including
incorporation of many types of modifications into proteins, enhancing our ability to control site-specificity
of protein posttranslational modifications (PTMs), modify protein backbones and introduce photocrosslinking probes. For PDZ (postsynaptic density protein, disks large, zonula occludens) protein domains,
expressed protein ligation (EPL) has been employed to introduce analogs of cognate amino acids, amideto-ester bond mutations, and phosphorylations in the study of PDZ domain-mediated protein-protein
interactions (PPIs). Here, we present protocols for EPL of PDZ domains focusing on phosphorylation and
amide-to-ester modifications in the PDZ domain proteins.
Key words Expressed protein ligation, Protein modification, Phosphorylation, Amide-to-ester mutation, Solid-phase peptide synthesis, Native chemical ligation
1
Introduction
The introduction of chemical modifications into proteins has broad
applicability for not only biotechnological and biomedical purposes
but also in studies of fundamental biological functions such as cell
signaling, protein-protein interactions (PPIs), and posttranslational
modifications (PTMs). Chemical synthesis of proteins via solidphase peptide synthesis (SPPS) enables veritably any modification
at any desired position [1, 2] and allows both the introduction of
noncanonical amino acids as well as peptide or protein backbone
modifications. The seemingly limitless scope of chemical space that
can be leveraged in SPPS is a major advantage over recombinant
methods to introduce protein modifications. However, the constraints in the coupling efficiency of longer peptide chains severely
restrict the size of peptides or proteins that can be generated by
SPPS. Undeniably, improvements in 9-fluorenylmethoxycarbonyl
(Fmoc)/tert-butyl (t-Bu)-SPPS and tert-butyloxycarbonyl (Boc)/
benzyl (Bzl)-SPPS, such as building blocks to minimize
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021
193
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Christin Kossmann et al.
aggregation, high-swelling resins, technological advancements in
automated SPPS, and increased purity of the protected amino acids
have advanced the previously set limit for peptides of 50–70 amino
acids, depending on the sequence [3–6]. Still, the synthesis of
proteins, such as PDZ domains that comprise typically 80–100
amino acids, is generally not feasible by SPPS.
In conjunction with SPPS, the development of native chemical
ligation (NCL), enabled by the pioneering work of Kent and colleagues in 1994, expanded the range of protein size that can be
synthesized. This reaction involves a chemoselective conjugation of
one peptide bearing a C-terminal α-thioester with a second peptide
bearing an N-terminal Cys, resulting in a native peptide bond
[7]. Proceeding initially through transthioesterification driven by
nucleophilic attack of the N-terminal Cys thiol moiety to the carbonyl carbon of the thioester, a new intermolecular thioester intermediate is formed. Subsequently, this thioester intermediate
rearranges through an S-to-N acyl shift by the α-amine of the
Cys. This leads to the formation of a native amide bond (see
Fig. 1) [8, 9]. For the synthesis of larger proteins, ligation of several
peptide fragments is possible, either by stepwise ligation from the
N- to the C-terminus or by one-pot ligations with Cys protection
groups [10–13]. By applying NCL, several small proteins have been
successfully synthesized, such as the HIV-1 protease covalent
dimer, encompassing 203 amino acids, generated from four synthetic peptide fragments [14].
However, the synthesis of larger proteins (>20 kDa) by NCL
remains challenging, because the multiple ligation steps impact the
overall yield, and certainly cannot match the yields of recombinant
systems. Thus, to address these concerns, expressed protein ligation
(EPL), a derivative of NCL, was developed to leverage the advantages of both recombinant protein expression and SPPS. EPL
involves a reaction between a recombinant protein fragment with
a synthetic peptide fragment containing the desired chemical modification [8, 15]. Multistep ligations involving the joining of more
than two fragments can be performed, but the reaction always
requires a combination of synthetic and recombinant components.
Generally, there are two types of EPL strategies that can be used for
targeting the insertion of modifications at different positions of the
protein: (1) modifications in the N-terminal region, where a synthetic peptide is ligated to a recombinant fragment with an
N-terminal Cys, and (2) modifications in the C-terminal region
where an N-terminal recombinant thioester protein is joined with a
synthetic peptide.
In the first strategy, a recombinant protein fragment with an
N-terminal Cys residue is generated typically by introduction of a
cleavage site, such as a protease recognition site, immediately adjacent to the N-terminal Cys. Then, proteases such as Factor Xa or
enterokinase cleave and reveal the N-terminal Cys residue (see
Fig. 2). The peptide thioester can be generated by Fmoc- as well
Synthetic PDZ Domains
195
Fig. 1 Mechanism of NCL. Initially, the C-terminal thioester of one peptide fragment undergoes a transthioesterification with the N-terminal Cys of a second peptide fragment. Secondly, an S-to-N acyl shift leads to the
formation of a native peptide bond between the two fragments
Fig. 2 Expressed protein ligation. Left: Strategy for N-terminal ligation. The N-terminal peptide fragment is
synthesized as a thioester and ligated to a recombinantly expressed protein, which is prior cleaved by Factor
Xa to result in an unprotected cysteine. Right: Strategy for C-terminal modification. The C-terminal peptide
fragment is synthesized and ligated to a recombinantly expressed protein which was designed with an intein
(fused to a chitin-binding domain (CBD)) to obtain a thioester after MESNa treatment
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Christin Kossmann et al.
Fig. 3 Strategies for the generation of peptide thioesters. (a) A thiol-linked resin is used for Boc-SPPS, which
reveals the functional thioester after HF cleavage. (b) In Fmoc-SPPS hydrazine resins can be used. The peptide
is elongated on the hydrazine linked resin. After TFA cleavage, a C-terminal hydrazide is generated, which can
be oxidized to an azide. By adding thiol additives (e.g., MPAA, MESNa, TFET), thiolysis of the azide leads to the
functional thioester
as Boc-synthesis. For Boc-synthesis, a resin with a thioester linker is
used, which yields a functional thioester after the hydrofluoric acid
(HF) cleavage. Due to instability of the thioester moiety in basic
conditions during the deprotection steps in Fmoc-synthesis, peptide thioesters need to be generated only after peptide chain elongation. The most common methods employ the use of N-acylurea
[16] or hydrazine linkers [17], both resulting in a thioester moiety
after activation of the trifluoroacetic acid (TFA)-cleaved peptide
fragment (see Fig. 3).
For the second strategy that generates C-terminally modified
proteins, a method was established in 1998 by Muir et al. that
adapted biologically occurring protein splicing reactions to create
recombinant proteins with a reactive α-thioester moiety [18]. Critical residues that are nucleophiles of the C-extein are mutated to
unreactive amino acids such as Ala. This arrests the reaction at the
initial thioester formation step upon intein cleavage with the addition of a nucleophilic thiol, such as the sodium salt of mercaptoethane sulfonate (MESNa) [8, 18]. This protein thioester is
then isolated and ligated to a synthetic peptide with an
N-terminal Cys, which is typically synthesized using Fmoc-based
SPPS. With these EPL strategies, larger protein fragments can be
generated efficiently through recombinant techniques while allowing the introduction of chemical modifications through the synthetic peptide with site-specificity.
There are several considerations when planning protein semisynthesis by EPL strategies. Firstly, semisynthetic proteins have to
be refolded after ligation as EPL is typically conducted under
denaturing conditions, which can be a challenge. Another consideration is the length of the synthetic peptide(s) and EPL is best
applied to the generation of proteins with modifications in the Nor C-terminal regions, to avoid ligation of multiple fragments. The
generation of thioesters is also only limited to certain residues
because some residues are either incompatible (Asp, Glu, Asn,
Pro, and Gln) or result in unfavorable EPL reaction kinetics (Ile,
Lys, Leu, Thr, and Val). Additionally, the requirement of a Cys
Synthetic PDZ Domains
197
residue at the ligation junction can be a limitation if an appropriate
ligation site with a native Cys residue cannot be identified. In this
case, a Cys mutation has to be introduced at the desired ligation
site, which may not be possible due to a consequently building of
sulfide bridges/heteromerization and eventually loss of protein
function. However, a number of approaches involving ligation
auxiliaries, Cys surrogates, and desulfurization methods, have
been developed to allow more flexibility in the selection of ligation
sites besides Cys [11, 19–23]. With these improvements, EPL has
become applicable for the generation of a wider spectrum of
proteins.
Several types of chemical modifications have been incorporated
into PDZ domains using both strategies of EPL to investigate sidechain interactions [24], backbone interactions [25], and phosphorylations [26]. Studies of backbone H-bond interactions are especially crucial for PDZ binding because the binding of C-terminal
peptide ligands to the PDZ protein domain is facilitated by a
conserved carboxylate-binding site via backbone hydrogen amide
bonds [25]. This was deciphered by the introduction of amide-toester mutations to these and other regions of PDZ domains
through EPL. Peptide fragments containing backbone amide-toester mutations (depsipeptides) were ligated to a recombinantly
expressed protein fragment, enabling the investigation of specific
backbone hydrogen bonds in the PDZ domain protein [25].
EPL also enabled the generation of semisynthetic phosphorylated PDZ domains, specifically of PDZ domains of the postsynaptic density protein 95 (PSD-95), which allowed its
phosphoregulated PPIs to be studied [26, 27]. A riveting feature
of this work is the direct comparison of the effects of introducing
phosphomimetics versus a semisynthetic protein with a ‘true’ phosphorylation. Although phosphomimetics, Glu and Asp, are used
often to study this important PTM, structural and chemical differences of the mimetics could render them as inadequate substitutions, especially in cases involving phosphotyrosine. In contrast,
in vitro phosphorylation uses kinases to install phosphate-groups
onto proteins, but this would not be feasible in certain cases where
the enzyme is unknown, or site-specificity is desired, but the
enzyme modifies multiple sites in the same protein. Recently, an
alternative method leverages a genetic code expansion technique to
incorporate more than 200 nonproteinogenic amino acids, including phosphorylated amino acids [28]. However, an enigmatic challenge with this technology is that not all protein sites are amenable
to modifications, and the reason for this is still in contention. In
these situations, EPL is an attractive option to introduce phosphorylation site-specifically to proteins in a reliable manner.
Herein, we provide protocols that have successfully generated
PDZ domains with modifications in either the N- or C-terminal
regions. Specifically, synthesis of peptide thioesters by Boc- and
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Christin Kossmann et al.
Fmoc-SPPS via the hydrazine method are described along with the
expression of C-terminal thioesters via intein fusion proteins and
N-terminal Cys containing proteins. PDZ domains inconveniently
lack a Cys residue where we desire to place a ligation junction, but
we circumvent this by employing radical desulfurization, which will
also be described in these protocols.
2
Materials
2.1 Plasmid
Construction
1. Isopropyl β-D-1-thiogalactopyranoside (IPTG) inducible bacterial expression vector with ampicillin resistance that encodes
the codon-optimized DNA construct of the target protein
fused to a polyhistidine purification tag, for example pRSET
(PDZ) (see Note 1).
2. Intein-encoding plasmid with ampicillin resistance for IPTG
inducible bacterial expression, for example pTWIN1.
3. 10 μM oligonucleotide primer stock solutions in nuclease-free
water (see Table 1).
4. Site-directed mutagenesis kit based on a high-fidelity DNA
polymerase.
5. Nuclease-free water.
6. Chemically competent E. coli cloning strain (TOP10).
7. S.O.C medium.
8. DNA miniprep kit.
9. 50 mg/mL ampicillin stock solution in ultrapure water.
10. Sterile Luria Broth (LB) agar plates: mix LB agar into ultrapure
water. Autoclave at 121 C for 30 min and cool down to 50 C
before adding ampicillin (100 μg/mL) and distributing on
92 mm x 16 mm sterile clear petri dishes. Store plates at 4 C.
11. Sterile Luria Broth (LB) media.
12. High-fidelity DNA polymerase and appropriate buffers.
13. NdeI and SapI restriction enzymes.
14. TAE buffer: 1 M ethylenediamine tetraacetic acid, 0.04 M Trisbase, 0.01 M acetic acid.
15. 1.5% agarose gel, prepared by melting solid agarose (ultrapure
agarose) suspended in TAE buffer using a microwave oven.
16. DNA gel purification kit.
17. T4 DNA ligase and appropriate buffers.
2.2 Protein
Expression
1. Chemically competent E. coli expression strain (BL21
dE3 pLys).
2. Sterile LB agar plates (see Subheading 2.1).
Synthetic PDZ Domains
199
Table 1
Primers used for the construction of plasmids encoding recombinant EPL fragments
Forward
1. Primers for introduction of factor Xa site
placed adjacent to the N-terminal Cysa,b
0
5 -(NNN)5 ATC GAG
GGA AGG TGC
(NNN)5-3´
2. Primers for PDZ cloning into pTWIN1c,d 50 -GGA ATT CAT ATG
(NNN)10-3´
Reverse
50 -(NNN)5 GCA CCT
TCC CTC GAT
(NNN)5-3´
50 -GGT GGT TGC TCT
TCC (NNN)6-3´
a
Bold letters highlight the gene encoding factor Xa site and a Cys residue
(NNN)x corresponds to flanking codons from the target gene, with x being the number of codons
c
NdeI site is underlined in the sequence
d
SapI site is italicized in the sequence
b
3. Sterilized Luria Broth (pH 7.2): dissolve premixed LB broth in
1 L of ultrapure water according to manufacturer’s instructions
and autoclave at 121 C for 30 min.
4. Sterilized 300 mL and 5 L culture flasks, autoclaved at 121 C
for 30 min.
5. 50 mg/mL ampicillin stock solution in ultrapure water.
6. 1 M IPTG stock solution in ultrapure water.
2.3 Protein
Purification
1. Elution buffer (50 mL): 50 mM sodium phosphate pH 7.4,
250 mM imidazole.
2. HisTrap Fast Flow (FF) columns, 5 mL.
3. Lysis Buffer (50 mL): 50 mM sodium phosphate pH 7.4,
10 mM MgCl2, 25 μg/mL DNase, complete protease inhibitor
(1 tablet/50 mL). Add DNase and protease inhibitor right
before use.
4. Wash buffer (200 mL): 50 mM sodium phosphate pH 7.4,
25 mM imidazole.
2.4 Thioester
Generation
1. Thiolysis buffer: 1–2 M urea, 50 mM sodium phosphate
pH 6.8, 150 mM NaCl.
2. Dialysis membrane or device, with molecular weight cutoff
(MWCO) of 10 kDa.
3. Sodium 2-mercaptoethanesulfonate (MESNa).
2.5 Factor Xa
Cleavage
1. Cleavage buffer: 50 mM Tris–HCl pH 8, 100 mM NaCl, 6 mM
calcium chloride.
2. Factor Xa protease.
3. Dialysis membrane or device, with a MWCO of 10 kDa.
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Christin Kossmann et al.
2.6 Solid-Phase
Peptide Synthesis
1. 2-(tritylmercapto)acetyl-L-leucinyl-PAM (Trt-S-Ac-L-Leu-PAM)
resin for Boc-based synthesis of peptide thioesters.
2. 2-clorotrityl chloride (2-CTC) resin on polystyrene for Fmocbased chemical synthesis of peptide hydrazides.
3. Preloaded Wang resin for Fmoc-based synthesis.
4. Fmoc-PAL-PEG-PS™ for Fmoc-based chemical synthesis of
long peptides.
5. Phenylacetamidomethyl (PAM) resin for Boc-based synthesis.
6. Fmoc/Boc-protected canonical amino acids.
7. Fmoc-Ser(PO(OBzl)OH)-OH, Fmoc-Thr(PO(OBzl)OH)OH, Fmoc-Tyr(PO(OBzl)OH)-OH for phosphopeptides.
8. α-hydroxy amino acids (see Note 2) for depsipeptides.
9. 2-(1-benzotriazole-1-yl)-1,1,3,3-tetramethyluronium hexafluorophosphate (HBTU) prepared as a 0.5 M stock solution
in DMF in a light-protected bottle.
10. Diisopropylamine (DIEA).
11. 20% (v/v) piperidine in DMF.
12. Fmoc-cleavage mix: 82.5% (v/v) TFA, 5% (v/v) phenol, 5%
(v/v) H2O, 5% (v/v) thioanisole, 2.5% (v/v) 1,2-ethanedithiol
(EDT). Prepare fresh.
13. Peptide dissolution buffer: 20% MeCN, 0.1% TFA.
14. Ninhydrin test/Kaiser test kit.
15. Disposable Reaction Tube (10 mL) (SiliCycle Inc.).
2.7 Peptide
Purification
1. High-pressure liquid chromatography (HPLC) buffer A: 5%
(v/v) acetonitrile (MeCN), 0.1% (v/v) TFA in ultrapure water,
filtered with 0.2 μm filters.
2. HPLC Buffer B: 95% MeCN, 0.1% TFA in ultrapure water,
filtered with 0.2 μm filters.
2.8 Expressed
Protein Ligation
1. NaNO2 stock solution: 0.2 M in ultrapure water. Prepare fresh.
2. Dithiothreitol (DTT) stock solution: 1 M in ultrapure water.
Can be stored at 20 C.
3. 300 mM Tris(2-carboxyethyl)phosphine
(TCEP) stock solution in ultrapure water.
hydrochloride
4. Ligation buffer 1: 0.2 M sodium phosphate pH 3.0, 6 M
guanidinium hydrochloride (Gu·HCl).
5. Ligation buffer 2: 0.2 M 4-mercaptophenylacetic acid (MPAA)
in 6 M Gu·HCl, 0.2 M sodium phosphate pH 7.0. Prepare
fresh.
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2.9
Desulfurization
201
1. Solubilizing buffer: 0.2 M sodium phosphate buffer pH 7, 6 M
Gu·HCl.
2. Radical initiator VA-044.
3. Reduced glutathione.
4. 0.5 M TCEP stock solution in 500 μl 6 M Gu·HCl, 0.2 M
sodium phosphate pH 7.
2.10
Equipment
1. Mass Spectrometer with electron spray ionization (ESI) coupled to an HPLC system (ESI-LC/MS), equipped with a C18
reversed-phase column for the analysis of peptides and a C8
reversed-phase column for the characterization of proteins,
using a linear gradient of a binary buffer system containing
H2O/MeCN/TFA (A: 95/5/0.1; B: 5/95/0.1) at a flow
rate of 1 mL/min for proteins and H2O/MeCN/formic acid
(A: 95/5/0.1; B: 5/95/0.1) at a flow rate of 1 mL/min for
peptides.
2. Fast protein liquid chromatography (FPLC) system, equipped
with a gel filtration column for size-exclusion chromatography
(SEC), operating at a flow rate of 1 mL/min.
3. Cell disruptor system.
4. 4–15 L Freeze Dryer.
5. MiniBlock Shaker.
6. Benchtop pH Meter.
7. Analytical reversed-phase ultra-high performance liquid chromatography (RP-UPLC) system with a C18 reversed-phase
column for the analysis of peptides and a C8 reversed-phase
column for the analysis of proteins, using a binary buffer system
consisting of H2O/MeCN/TFA (A: 95/5/0.1; B: 5/95/0.1)
at 0.45 mL/min.
8. Preparative reversed-phase high-performance liquid chromatography (RP-HPLC) system with a C18 reversed-phase column for the purification of peptides and a reversed-phase C4
column for the purification of proteins, using a linear gradient
of a binary solvent system of H2O/MeCN/TFA (A: 95/5/
0.1; B: 5/95/0.1) at a flow rate of 20 mL/min.
3
Methods
3.1 Plasmid
Construction
for Expression
of Recombinant
Fragments
Prior to moving forward with the protocols below, a bacterial
expression vector with a gene encoding the protein-of-interest has
to be procured or cloned. Our PDZ domains are encoded in a
pRSET vector, so the following steps will refer to this plasmid,
pRSET(PDZ), but other constructs can be used as well.
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3.1.1 Cloning of Plasmid
Encoding C-Terminal
Fragment
with N-Terminal Cys
For the generation of recombinant C-terminal fragment, the protein is expressed with a hexa-histidine purification tag and a recognition site for factor Xa, which upon cleavage after protein isolation
reveals the N-terminal Cys. The following protocol details the
cloning strategy, utilizing site-directed mutagenesis, to obtain the
plasmid encoding the C-fragment, pRSET(ΔCPDZ), starting with
the construct that encodes the full-length protein, pRSET(PDZ).
1. In a PCR tube, combine factor Xa primer pairs from Table 1,
the plasmid encoding full-length protein pRSET(PDZ), and
appropriate reagents, including a high-fidelity DNA polymerase from a site-directed mutagenesis kit.
2. Load PCR samples onto a thermocycler and execute temperature cycles according to manufacturer’s instructions.
3. To digest the DNA template, add 1 μL of DpnI restriction
enzyme to PCR mixture and let incubate at 37 C overnight, or
at least 8 h.
4. Transform 1 μL of PCR digest into 10 μL chemically competent TOP10 E. coli cells. After a heat-shock treatment of 45 s at
42 C followed by 2 min incubation on ice, grow cells in
250 μL of S.O.C medium at 37 C and 200 rpm for at least
45 min.
5. Transfer cells to sterile LB-ampicillin agar plates and incubate
overnight at 37 C.
6. Inoculate one colony into 5 mL of sterile LB-ampicillin media
and incubate overnight at 37 C and 200 rpm.
7. Isolate plasmid with DNA miniprep kit using manufacturer’s
instructions.
8. Confirm plasmid sequence with DNA sequencing to verify that
the mutations were inserted correctly into the plasmid and
store DNA at 20 C (see Note 3).
3.1.2 Gene Insertion into
an Intein-Encoding Plasmid
for the Generation
of Protein Thioesters
Below, is a protocol to prepare pTWIN1-His7(ΔNPDZ) by cloning
the PDZ domain fragment into a Mxe GyrA-encoding pTWIN1His7 vector, which has been modified to contain a heptahistidine
tag. The original pTWIN1 vector with a chitin-binding domain
(CBD) that leverages the IMPACT protein purification system can
be used as well.
1. Using primer pairs from Table 1, amplify the gene encoding the
PDZ domain fragment with a high-fidelity DNA polymerase,
following manufacturer’s instructions for reagent amounts and
thermal cycles.
2. Digest PCR fragment and pTWIN1-His7 vector with 1 μL each
of NdeI and SapI restriction enzymes at 37 C for 2 h. Isolate
digestion products with DNA gel electrophoresis. Extract fragment from agarose gel with DNA gel purification kit.
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203
3. Ligate the PCR fragment into pTWIN1 vector with T4 DNA
ligase according to the manufacturer’s instructions. This forms
the plasmid encoding the PDZ domain fragment fused to the
intein, pTWIN1-His7(ΔNPDZ).
4. Transform 1 μL of ligation mixture into 10 μL of chemically
competent E. coli cloning strain, TOP10. After growing the
culture in S.O.C. media for 45 min, apply 50 μL of cells onto an
LB agar plate with 100 μg/mL ampicillin. Allow colonies to
form overnight in an incubator at 37 C.
5. Prepare 5 mL of overnight culture in LB medium with
100 μg/mL ampicillin by inoculating one colony from the
LB-ampicillin plate. Allow the culture to grow overnight in a
shaking incubator at 37 C at 200 rpm.
6. Isolate plasmid, pTWIN1-His7(ΔNPDZ), from overnight culture with a DNA miniprep kit, according to manufacturer’s
instructions. Verify plasmid with DNA sequencing and store at
20 C.
3.2 Protein
Expression
The following steps can be applied to the expression of both the
N- and C-terminal recombinant fragments.
1. Transform 1 μL DNA (obtained in Subheading 3.1) into 10 μL
of chemically competent E. coli expression strain, BL21 dE3
pLys. After growing in 100 μL of S.O.C. media for 45 min at
37 C and shaking at 200 rpm, spread 10 μL of cells onto an LB
agar plate containing 100 μg/mL ampicillin. Allow colonies to
form overnight in an incubator at 37 C.
2. Prepare the starting culture by inoculating one colony from the
LB-ampicillin plate into a 300-mL flask containing 100 mL of
sterile LB broth and allow the culture to grow overnight in a
shaking incubator at 37 C and shaking at 200 rpm.
3. Measure the optical density of the starting culture at the wavelength of 600 nm (OD600) with a UV spectrophotometer. Add
starting culture to prewarmed 5 L flasks containing 1 L LB
broth with 100 μg/mL ampicillin to target a starting OD600
value of 0.1.
4. Allow culture to grow in a shaking incubator at 37 C and
200 rpm until the OD600 value reaches between 0.4 and 0.8.
Induce expression by adding IPTG stock to target a final
concentration of 1 mM to the culture. Then allow culture to
grow for another 4 h at 37 C and shaking at 200 rpm (see Note
4).
5. Harvest cells by centrifuging the culture at 10,000 g for
10 min at 4 C. Discard supernatant and collect pellet. Store
cell pellets at 20 C for later processing or proceed directly
into the next protein purification steps.
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3.3 Protein
Purification from
Bacterial Lysates
The protocols below are applicable to the purification of N- and
C-terminal recombinant fragments.
3.3.1 Cell Lysis
1. Thaw cell pellet on ice and add 5–10 mL ice-cold lysis buffer
per g pellet to the thawed cell pellet.
2. Solubilize the pellet until a homogeneous solution is reached.
A magnetic stirrer may be used for effective solubilization.
3. Lyse cells by using a cell disrupter at 4 C, applying a pressure
of 26 kpsi, and repeat this step once.
4. Pellet cell debris by centrifugation for 30 min, at 4 C and
30,000 g (see Note 5).
3.3.2 His-Trap
Purification
1. Filter cell lysate using a syringe and a 0.45 μm syringe filter.
Subsequently, dilute it 2/1 in wash buffer. Maintain the lysate
sample at 0–4 C during the entire purification process by
performing the next steps in a refrigerator and keeping samples
on ice.
2. Measure the pH of the sample and adjust it to pH 7.4 with 1 M
HCl or NaOH (see Note 6).
3. Prepare a 5-mL His-trap FF column by washing it with 3 column volumes (CV) deionized water and equilibrating it with
5 CV wash buffer. This can be performed with either a peristaltic pump with a flow rate of 3.5 mL/min or manually with a
syringe.
4. Load the filtered and diluted lysate sample onto the column
and remove nonbinding residues by washing it with 20 CV
wash buffer.
5. Elute the His-tag protein with 5 CV elution buffer and collect
the flow-through in 1.5 mL fractions in 2 mL reaction tubes.
6. Measure the protein concentration of each fraction on a spectrophotometer at 280 nm. Then verify purity and molecular
weight of the protein with UPLC and LCMS, respectively.
Pool pure fractions to target a purity of >85–90% from
UPLC (see Note 7).
7. Concentrate the combined sample using centrifugal filters with
a MWCO below the molecular weight of the respective
protein.
3.3.3 FPLC Purification
If the protein sample does not meet target purity of >85–90% after
the His-trap purification, a size-exclusion (SEC) purification step
can be performed using an FPLC. A flow rate of 1 mL/min at 4 C
is recommended.
1. Equilibrate the column with 5 CV wash buffer.
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205
2. Load the sample onto the column using a syringe and wash the
column with 8–10 CV of wash buffer.
3. Elute the protein with 5 CV wash buffer and collect 1.5 mL
fractions in 2 mL reaction tubes.
4. Measure the protein concentration of each fraction on a spectrophotometer at 280 nm. Then verify purity and molecular
weight of the protein with UPLC and LCMS, respectively.
Pool pure fractions to target a purity of >85–90% from UPLC.
5. Concentrate the combined sample using Amicon® Ultra Centrifugal Filters with the suitable MWCO and measure the final
sample on LC-MS and UPLC.
3.4 Protein Thioester
ΔNPDZ Formation
and Isolation
1. Dialyze recombinant protein from pTWIN1-His7(ΔNPDZ)
against thiolysis buffer in a 1 L beaker. Replace dialysis buffer
with fresh thiolysis buffer every 2 h for at least 2 times. After
the last buffer exchange, allow the sample to dialyze overnight,
or about 8–12 h.
2. Transfer dialyzed protein sample into a 50 mL-conical tube and
add MESNa powder directly into the tube to target 100 mM
concentration. Adjust pH to 6.8 with 1 M HCl or NaOH, if
necessary.
3. Let thiolysis reaction proceed to form the protein thioester on a
rocking table at 4 C, for 0.5–3 days, as reaction time is highly
dependent on the intein and the amino acid at the cleavage
junction (see Note 8). Monitor reaction by removing 10 μL
aliquots every hour and analyze on LC-MS. Meanwhile, equilibrate a 5-mL His-trap FF column according to Subheading
3.3.2, step 3 with thiolysis buffer in preparation for protein
thioester purification.
4. Purify reaction mixture immediately upon completion (see
Note 9), by flowing the reaction mix through a His-trap FF
column, which sequesters the cleaved, histidine-tagged intein
and any uncleaved fusion protein. Collect eluent, which contains the protein thioester. Continue to elute with 5 CV thiolysis buffer and collect 1 CV per fraction.
5. Analyze fractions with LC-MS and pool the cleanest fractions.
If the final sample is below the target purity of 80% after this
step, the protein thioester can be further purified using SEC
(see Subheading 3.3.3).
6. Dialyze protein thioester against ultrapure water to remove
salts and buffer components. Freeze dry and lyophilize sample
to produce white solids. Characterize the final product with
UPLC and LC-MS. Store at 20 C until they are ready to be
used for processing.
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3.5 Factor Xa
Cleavage to Generate
N-Terminal Cys
Protein Fragment
ΔCPDZ
1. Dialyze recombinant protein from pRSET(ΔCPDZ) against
1 L of factor Xa buffer at 4 C. Exchange dialysis buffer with
fresh buffer every 2 h for 3 times. Allow sample to dialyze
overnight after the last buffer exchange.
2. Transfer protein sample to a fresh 50 mL-conical tube. Add
factor Xa according to manufacturer’s instructions and allow
enzymatic cleavage to progress at 4 C on a rotating or rocking
table for 12–24 h. Remove 10 μL aliquots every hour and
monitor reaction with LC-MS (see Note 10).
3. Prepare a 5-mL His-trap FF column for purification by following Subheading 3.3.2, step 3 with cleavage buffer. Purify
cleavage mixture immediately after reaction is complete to
avoid nonspecific cleavage, using steps 4 and 5 in Subheading
3.4 with factor Xa cleavage buffer as the eluent.
4. Dialyze the final protein fragment against ultrapure water to
remove salt and buffer components, and lyophilize protein to
form white amorphous solids. Analyze compound with UPLC
and LC-MS, and store at 20 C until they are ready to be used
for processing.
3.6 Solid-Phase
Peptide Synthesis
SPPS can be performed with two protection techniques: Boc/Bzl
or Fmoc/t-Bu. The latter has the advantage of an orthogonal
protection system, which allows milder conditions and a broader
range in the pH-dependent reaction conditions for selective cleavage of side-chain protection groups and the Fmoc-group [3]. The
Fmoc-SPPS method is preferred over Boc-SPPS in regard to phosphorylated peptides because phosphate groups are not stable during HF cleavage. Several automated synthesizers are available,
enabling fast and convenient SPPS. For the inclusion of expensive
building blocks, such as phospho-amino acids, manual synthesis is
often preferred, because less material is needed and easy direct
monitoring of the reaction efficiency can be performed via Kaiser
test or test cleavage (see Note 11). To insert amide-to ester mutations, Boc-SPPS is the method of choice over Fmoc-SPPS, because
the basic Nα-deprotection step in Fmoc-SPPS can lead to hydrolysis
of the ester group.
Different resins are used for the synthesis of N- and C-terminal
fragments. C-terminal fragments do not need special requirements
for the resin and can be generated on commercially available resins
(see Note 12). For N-terminal fragments, the generation of a
C-terminal thioester is essential for the ligation reaction. Therefore,
functional resins are used, for example, comprising a hydrazine
linker in Fmoc-SPPS or thioester generating resins in Boc-SPPS.
3.7 Fmoc/t-Bu-SPPS
for the Synthesis
of Phosphopeptides
The protocol below, for Fmoc-SPPS is suitable for the synthesis of
peptides with canonical amino acids and for inserting modifications
like glycosylations and phosphorylations. We provide a standard
protocol for Fmoc-SPPS, including the example of the introduction of phospho-amino acid building blocks.
Synthetic PDZ Domains
207
Fig. 4 Fmoc-hydrazine coupling to 2-CTC resin. (a) Structural formula and reaction conditions, (b) Experimental setup
3.7.1 Resin Loading
For synthetic N-terminal peptide fragments, a functional hydrazine
linker is essential for the ligation reaction. Fmoc-hydrazono-pyruvyl-aminomethyl polystyrene resins are commercially available, with
the first amino acid precoupled for all standard L-amino acids. If a
noncanonical first amino acid is needed, manual preloading of
2-chlorotrityl chloride (2-CTC) resin applying the below protocol
can be performed (see Fig. 4).
1. Swell 2-CTC resin (2 g) in DCM (16 mL) at 0 C in a round
bottom flask in an ice-bath for at least 30 min.
2. Dissolve Fmoc-hydrazine hydrochloride (4 eq) and DIEA
(10 eq) in DMF (20 mL) and CH2Cl2 (4 mL).
3. Add the solution from step 2 dropwise to the resin slurry at
0 C and stir continuously and gently overnight from 0 C to
room temperature (RT).
4. Add 80 eq methanol (0.32 mL) to quench the nonreacted
2-CTC resin. Subsequently, wash the resin three times with
each: 5 mL DMF, 5 mL H2O, 5 mL methanol, 5 mL ethyl
ether and keep under high vacuum for at least 1 h for complete
drying and store at 4 C [17].
5. Test the resin loading via Fmoc-quantification by first weighing
out ~10 mg dry resin per sample into a 1.5 mL reaction tube.
Add 1 mL of 20% (v/v) piperidine in DMF and incubate for
10 min while shaking at 500 rpm at 25 C to completely
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Christin Kossmann et al.
remove all Fmoc-groups. Filter the solution with a 0.2 μm
syringe filter and dilute the filtered liquid 1/50 and 1/100 in
methanol to a total volume of 1 mL. Additionally, prepare
blanks for each dilution with 20% (v/v) piperidine. Measure
the absorbance at 298.8 nm in a silica cuvette and calculate the
resin loading with the equation [29]:
A
½nmV sample ½Ll ½cmD106
loading mmol g1 ¼ 298:8
ε298:8 ½Lmol1 cm1 mresin ½mg
where:
A298.8 ¼ Absorption of the sample at 298.8 nm.
Vsample ¼ Sample volume [L].
L ¼ Optical path length of the cell (e.g., 1 cm) [cm].
D ¼ Dilution factor.
106 ¼ Conversion factor of mol to mmol and mg1 to g1.
ε298.8 ¼ Molar absorption coefficient of Fmoc at 298.8 ¼ 6089
[L mol1 cm1].
3.7.2 Fmoc-SPPS
Coupling Cycle
A fritted reaction vessel with a cap and a septum, combined with a
MiniBlock system (Mettler Toledo, Columbia MD, USA), is convenient equipment for peptide synthesis. With this equipment
setup, multiple peptides can be synthesized in parallel, and excess
solvents can be flushed out into a collection flask with a flow of air
or nitrogen gas (see Note 13).
We use a 0.1 mmol scale as a standard scale, thus the amounts
mentioned in the following protocol refer to a 0.1 mmol scale.
Amino acid coupling can be performed with several coupling
reagents. The following steps describe the use of HBTU and
DIEA. The ratio of amino acid/HBTU/DIEA is 4/4/8 equivalents relative to the resin loading.
1. Place the resin in a fritted reaction vessel equipped with a
bottom cap and a septum. Close the vessel with the bottom
cap and add 2 mL DMF. The resin must be completely covered.
Let the resin swell for at least 20 min.
2. If a precoupled Fmoc-protected resin is used, the synthesis
starts with the deprotection of the precoupled amino acid. If
no Fmoc-protection group is attached, proceed directly with
the coupling step (see step 6).
3. Add 2 mL 20% piperidine to the resin-bound peptide and
incubate, while shaking for 2 min on a MiniBlock system with
450 rpm. Subsequently, drain the solvents by flushing it with
air through the filter.
4. Repeat step 3 to ensure complete Fmoc deprotection.
Synthetic PDZ Domains
209
5. Flow-wash with DMF for 1 min before continuing with the
coupling of the next amino acid.
6. Dissolve 4 eq Fmoc-protected amino acid in 4 eq HBTU
(0.8 mL) from the stock solution in a 10 mL glass vial. After
complete dissolution, add 8 eq. (0.14 mL) DIEA into the
mixture to preactivate the amino acid for 2 min (see Note
14). Transfer the preactivated amino acid mixture to the reaction vessel containing the deprotected resin-bound peptide (see
Note 15).
7. For the introduction of the phosphorylated amino acid perform step 4, but with 4 eq Fmoc-protected phosphorylated
building block and dissolve them with 4 eq. 1-[Bis
(dimethylamino)methylene]-1H-1,2,3-triazolo[4,5-b]pyridinium 3-oxid hexafluorophosphate (HATU) (152 mg) in
0.8 mL DMF. After complete dissolution, add 8 eq.
(0.14 mL) DIEA, mix and preactivate for 2 min. Transfer the
preactivated amino acid mixture to the reaction vessel containing the deprotected resin-bound peptide.
8. Close the reaction vessel with a septum and incubate under
shaking at RT for 1 h (see Note 16).
9. Flow-wash with DMF for 30 s to remove excess coupling
mixture and by-products (see Note 17).
10. Coupling efficiency can be tested via a Kaiser test (see Note
18). If coupling is incomplete, indicated by a blue color of the
resin beads, repeat coupling. If the Kaiser test is negative
(no blue color change), continue with deprotection and coupling of the next amino acid (see Note 19).
3.7.3 Cleavage
and Global Deprotection
1. Prepare 5 mL cleavage mix (TFA/phenol/H2O/thioanisole/
EDT 82.5/5/5/5/2.5) per 0.1 mmol scale synthesis (see Note
20) [30].
2. Add 5 mL cleavage mixture to the deprotected peptide and
incubate for 1–2.5 h while shaking (see Note 21).
3. Transfer the cleavage solution into a 50 mL-conical tube by
filtering it through a reaction vessel, equipped with a filter. To
achieve full recovery, flush subsequently 2 with TFA/DCM
(0.3–1 mL in total).
4. Precipitate the peptide with 30–40 mL ice-cold diethyl ether
and spin it down by centrifuging 7 min with 3500 g at 4 C
(see Note 22). Remove the clear supernatant by decanting and
dissolve the peptide pellet in a solution of 25% acetonitrile,
0.1% TFA in ultrapure water. Lyophilize the peptide to obtain
white solids, which can be stored long-term at 20 C (see
Note 23) until further use.
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5. Dissolve the pellet in 20% MeCN, 0.1% TFA in water, filter
with a 0.2 μm syringe filter, and purify with preparative
RP-HPLC, applying a linear gradient of a binary solvent system
of H2O–MeCN–TFA (A: 95:5:0.1; B: 5:95:0.1) at a flow rate
of 20 mL/min.
6. Lyophilize the purified sample to obtain white solids, which
can be stored long-term at 20 C until further use.
3.8 Boc/Bzl-SPPS
for the Insertion
of Amide-to Ester
Mutations
The insertion of an ester bond into a peptide is usually performed
by coupling the α-hydroxy acid to the N-terminus of the growing
peptide. Subsequently, an ester bond is formed with the following
α-amino acid. Another option to incorporate amide-to-ester substitution is the use of a preformed dipeptide building block
[Boc-amino acid-(CO)O-amino acid-OH] in standard Boc-SPPS.
These building blocks are produced by solution-phase synthesis.
The protocols below detail the synthesis of α-hydroxy acids.
3.8.1 Synthesis
of α-Hydroxy Acids
1. Treat 1 mmol Boc-protected amino acid with 3 mL TFA for
15 min to remove the Boc protection group.
2. Evaporate TFA and dissolve the residue in a 4 mL mixture of
1:1 (v/v) dioxane and water and cool it to 0 C in an ice bath.
Subsequently, add 2.0 mmol tert-butylnitrite and incubate at
20 C for 1 h, constantly stirring.
3. Upon completion, remove the solvent in vacuo and purify the
resulting residue by silica gel chromatography (DCM/MeOH
10/1 as a standard method).
3.8.2 Boc-SPPS
Coupling Cycle
Merrifield (chloromethyl) or PAM (phenylacetamidomethyl) resins
are the most commonly used in the standard Boc-SPPS protocol
and suitable for N-terminal fragments [31]. For thioester peptides,
Trt-S-Ac-L-Leu-PAM resin is used, which results in a functional
thioester after HF cleavage.
In the protocol below, we use HBTU and DIEA for coupling in
a 0.1 mmol scale. The stoichiometric ratio of the reagents is 4/4/8
(AA/HBTU/DIEA) throughout the whole protocol (see Note
24).
1. Weigh out the desired resin in an adequate amount and transfer
it to a fritted reaction vessel with a stopcock and a Teflon screw
cap. After closing the vessel with the stopcock, fill in DMF until
the resin is completely covered. Let the resin swell for at least
30 min. If the resin is precoupled with the first Boc-protected
amino acid, proceed to the deprotection step. If no
Boc-protection group is attached, proceed directly with the
coupling step (step 6).
2. Flow wash the resin with DMF for 30 s (see Note 25).
Synthetic PDZ Domains
211
3. Close the reaction vessel with the stopcock and add 2 mL neat
TFA. Close the reaction vessel with the Teflon screw cap and
allow deprotection for 1 min.
4. Repeat the deprotection (step 2) to ensure complete
Boc-deprotection.
5. Flow wash the resin with DMF for 30 s.
6. Weigh 4 eq of Boc-protected amino acid into a 10 mL glass vial
and add 4 eq of HBTU (0.8 mL) from the 0.5 M stock
solution. Dissolve the amino acid by shaking.
7. After complete dissolution, add 8 eq DIEA (0.14 mL) to the
amino acid–HBTU mixture. Allow preactivation for 2 min and
transfer the solution to the resin.
8. Close the reaction vessel with the Teflon screw cap and shake
the mixture in a wrist arm shaker. An incubation time of
10–20 min is needed for complete coupling efficiency. After
10–20 min, the coupling efficiency can be tested with a Kaiser
test (see Note 18). If the coupling is shown to be incomplete by
a blue color change of the resin beads, repeat the coupling step.
In case of complete coupling (no blue color change), continue
with the deprotection step and the coupling of the next
amino acid.
3.8.3 Cleavage
and Global Deprotection
Cleavage and deprotection of peptides synthesized by the Boc/Bzl
is most efficiently done by the anhydrous hydrogen fluoride, a
strong acid that is volatile and toxic and must be handled in
specialized equipment. The protocol for HF cleavage is beyond
the scope of this chapter, but extensively described elsewhere [32].
3.9 Expressed
Protein Ligation
For both, C- and N-terminal modifications, the same ligation
protocol is used. If a modification is implemented in the
N-terminal peptide fragment, this fragment bears a thioester in
case of Boc-SPPS, or a hydrazine linker in case of Fmoc-SPPS.
For the latter option, activate the hydrazide via oxidation preliminary to the ligation step (see Subheading 3.9.1).
3.9.1 Oxidation
of Hydrazide Peptide
to Generate an Active
Thioester
1. Weight out the hydrazide peptide in a reaction tube and dissolve it to a final concentration of 4 mM in ligation buffer 1.
3.9.2 Ligation
1. Weigh out the fragments with a proportion of 1/1.2 of
C-terminal fragment to the N-terminal fragment.
2. Add 5 eq of the 0.2 M NaNO2 solution to the hydrazide
peptide and cool the reaction mixture to 0 C, while stirring
(see Note 26). Complete oxidation takes 20 min (see Note 27).
2. Dissolve the N-terminal thioester peptide to a final concentration of 2 mM in ligation buffer 2 and adjust pH to 6.0 with
NaOH (see Note 28).
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Christin Kossmann et al.
3. Add the C-terminal peptide to the reaction mixture and adjust
pH carefully to 6.8–7.0 with NaOH or HCl (see Note 29).
4. Incubate the reaction for 2–8 h at 25 C on a shaker at
500 rpm. If overnight reactions are necessary, incubate the
solution at 4 C while constant shaking.
5. Quench the reaction by adding 60 eq TCEP from the TCEP
stock solution.
6. The ligation product can be purified directly, either via sizeexclusion chromatography (see Subheading 3.3.3) or via
preparative HPLC.
3.10
Desulfurization
Since the native chemical ligation reaction often requires the mutation of Ala to Cys, a conversion of the Cys to Ala might be necessary
to remove EPL artifacts. This step can be performed by metal-free
desulfurization with TCEP, a radical initiator (VA-044) and
reduced glutathione.
1. Place the ligation product in a 1.5 mL reaction vessel and
dissolve it in solubilizing buffer to a final concentration of
2.5 mM.
2. Add 0.5 M TCEP solution to the peptide to achieve a final
TCEP concentration of 0.2 M. Mix by vortexing.
3. Add reduced glutathione (solid) to a final concentration of
0.04 M.
4. Add VA-044 (solid) to a final concentration of 0.02 M.
5. The reaction takes 5–20 h at RT, approximately 20 C, on a
shaker at 500 rpm (see Note 30).
6. Test the completion of desulfurization periodically by removing 10 μL sample and measure it by LC-MS and UPLC.
4
Notes
1. Procuring or constructing a plasmid that encodes the fulllength protein-of-interest will give flexibility in the cloning of
all recombinant fragments for EPL. For PDZ domains, we
purchased the full-length sequence of the PDZ domain and
cloned it into pRSET vector. This construct can be used either
in mutagenesis to generate a plasmid encoding C-fragment, or
as the DNA template in PCR to generate the fragment that
would be cloned into an intein-encoding plasmid.
2. Six (Ala, Gly, Phe, Ile, Leu, Met, Asn, Gln, and Val) of the
nineteen α-hydroxy acids are commercially available.
Synthetic PDZ Domains
213
3. We use T7-based expression vectors for PDZ domains, and
therefore T7 promoter forward (5- TAATACGACTCACTA
TAGGG -3) and the T7 terminator reverse (5- GCTAGT
TATTGCTCAGCGG -3) sequencing primers are typically
used to verify our plasmids.
4. Certain PDZ fragments when fused to an intein are expressed
as insoluble protein. Optimization of expression conditions
such as lowering temperature could allow expression of soluble
protein. Otherwise, it is possible to isolate and solubilize the
inclusion bodies with high concentration of denaturant (6 M
Gu·HCl or urea) prior to protein purification.
5. If the recombinant protein is expressed in inclusion bodies,
collect the protein pellet rather than the supernatant after
centrifugation. Then dissolve the pellet in lysis buffer supplemented with high concentrations of denaturant (e.g., 6 M
Gu·HCl). Incubate for 1 h until all particles are visually solubilized. Centrifuge the lysate at 50,000 g for 60 min and
collect supernatant. The lysate can then be purified using the
same IMAC steps but with elution buffers containing the same
concentration of denaturant in lysis buffer.
6. The pH should be adjusted to a pH above the pKa of His. pH 8
is attempted for enhancing the fraction of His being deprotonated and thus able to chelate to the Ni column.
7. The highest possible purity is desired to ensure a clean ligation
reaction with few site products and a better estimation of
equivalents used during NCL.
8. Reaction conditions may need to be optimized for different
proteins and inteins in order to maximize cleavage efficiency
while minimizing thioester hydrolysis. As a general rule, higher
thiolysis temperature, MESNa concentration, and reaction pH
would increase intein cleavage rate. However, if thioester
hydrolysis persists, adding urea to target a concentration
between 1–2 mM, as well as lowering reaction temperature
and pH can minimize the formation of the hydrolyzed side
product. For any pH adjustments performed during thiolysis, it
is also critical that the NaOH or HCl is added carefully so that
the reaction does not reach extreme ranges where the thioester
can hydrolyze.
9. If a pH adjustment is necessary before purification with a nickel
column, carefully apply 1 M NaOH or HCl to prevent the
sample from reaching extreme pH ranges, where the protein
thioester can hydrolyze.
10. Although performing the reaction at 4 C overnight, or
8–12 h, can prevent nonspecific cleavage of proteins, which is
known to occur in some PDZ domains, they can also result in
incomplete cleavage. To improve efficiency, more enzyme can
214
Christin Kossmann et al.
be used or the reaction can also be conducted at room temperature or ~20 C, but this would only be feasible for proteins
that are not as susceptible to nonspecific cleavage. Extended
reaction time and buffer optimization can also improve factor
Xa activity, or an alternative cleavage enzyme can be used
instead, such as SUMO protease.
11. A small-scale cleavage of 1–3 mg of resin is a helpful tool to test
the synthesis success. Truncations can be detected and
annotated.
12. Polystyrene resin is not suitable for long peptides. For long
peptides high swelling resins, like TG-Wang or ChemMatrix®
resins are recommended.
13. Alternatively, solvents can also be removed by applying
vacuum.
14. Insufficient preactivation or an excess of HBTU can lead to a
tetramethylguanidinium termination adduct on amine
(+98 Da).
15. The volume of the solution should cover the resin sufficiently
so that the resin flows freely during shaking. If the volume is
not sufficient, add DMF and prolong coupling time if necessary (test by Kaiser-test).
16. Amino acid coupling can also be performed with heating. This
decreases the coupling time. 90 C enables complete coupling
within 1 min, but fragile peptides may be degraded. With
heating at 70 C, 5 min coupling time is recommended.
17. For peptides containing oxygen-sensitive moieties (e.g., Met
or Cys) it is important not to flush the peptide with excess air,
to avoid oxidation. Flushing with nitrogen can be used as an
alternative.
18. To perform a Kaiser test, wash 1–2 mg resin with DCM
(3 1 mL) and proceed according to the manufacturer’s
instructions.
19. The synthesis can be stopped after any coupling step. In this
case, wash the resin 1 min with DMF and 1 min with DCM and
transfer the vessel to a desiccator.
20. Depending on the peptide properties, different cleavage mixtures may be needed [30].
21. For short peptides, a cleavage time of 1 h is sufficient. For
peptides >20 amino acids or Arg containing peptides, an
increased incubation time should be allowed. Increased temperature may also be used to shorten cleavage time.
22. Short peptides or very hydrophilic peptides may not fully
precipitate directly. In that case, let the solution rest on dry
ice for 1 h before continuing.
Synthetic PDZ Domains
215
23. A sample for quality measurements should be taken before
freezing the peptide. We run LC-MS and UPLC measurements
as a standard for all synthesized peptides after cleavage.
24. Boc-Asn is activated with HBTU and HOBt, Boc-Asn(Xan) is
activated with HBTU only.
25. For Gln couplings, DCM should be used for washing to avoid
Pca formation.
26. If high amounts of by-products appear, cool to 15 C by
using NaCl and ice.
27. The reaction can be monitored by converting the azide to a
thioester by adding DTT or MPAA and measuring it in LC-MS
and UPLC.
28. If an N-terminal Fmoc-synthesized hydrazine peptide is used
after activation via oxidation, add ligation buffer 2 to a final
concentration of 2 mM and remove cooling.
29. If the pH climbs very high above 7.0, the peptide thioester will
hydrolyze.
30. Depending on the protein stability it can also be incubated at
4 C or 37 C with increased or decreased reaction times,
respectively.
References
1. Merrifield RB (1963) Solid-phase peptide
synthesis. I. The synthesis of a tetrapeptide. J
Am Chem Soc 85:2149–2154
2. Palomo JM (2014) Solid-phase peptide synthesis: an overview focused on the preparation of
biologically relevant peptides. RSC Adv
4:32658–32672
3. Behrendt R, White P, Offer J (2016) Advances
in Fmoc solid-phase peptide synthesis. J Pept
Sci 22:4–27
4. Wöhr T, Mutter M (1995) Pseudo-prolines in
peptide synthesis: direct insertion of serine and
threonine derived oxazolidines in dipeptides.
Tetrahedron Lett 36:3847–3848
5. Spare LK, Laude V, Harman DG, AldrichWright JR, Gordon CP (2018) An optimised
approach for continuous-flow solid-phase peptide synthesis utilising a rudimentary flow reactor. React Chem Eng 3:875–882
6. Varela Y, Vanegas Murcia M, Patarroyo M
(2018) Synthetic evaluation of standard and
microwave-assisted solid-phase peptide synthesis of a long chimeric peptide derived from four
plasmodium falciparum proteins. Molecules
23:2877
7. Dawson PE, Kent SB (2000) Synthesis of
native proteins by chemical ligation. Annu
Rev Biochem 69:923–960
8. Conibear AC, Watson EE, Payne RJ, Becker
CF (2018) Native chemical ligation in protein
synthesis and semi-synthesis. Chem Soc Rev
47:9046–9068
9. Johnson EC, Kent SB (2006) Insights into the
mechanism and catalysis of the native chemical
ligation reaction. J Am Chem Soc
128:6640–6646
10. Li J, Li Y, He Q, Li Y, Li H, Liu L (2014)
One-pot native chemical ligation of peptide
hydrazides enables total synthesis of modified
histones. Org Biomol Chem 12:5435–5441
11. Thompson RE, Liu X, Alonso-Garcı́a N, Pereira PJB, Jolliffe KA, Payne RJ (2014) Trifluoroethanethiol: an additive for efficient one-pot
peptide ligation- desulfurization chemistry. J
Am Chem Soc 136:8161–8164
12. Huang YC, Chen CC, Gao S, Wang YH,
Xiao H, Wang F, Li YM (2016) Synthesis of
L-and D-ubiquitin by one-pot ligation and
metal-free desulfurization. Chem Eur J
22:7623–7628
13. Ghassemian A, Wang CIA, Yau MK, Reid RC,
Lewis RJ, Fairlie DP, Durek T (2013) Efficient
chemical synthesis of human complement protein C3a. Chem Commun 49:2356–2358
14. Torbeev VY, Kent SB (2007) Convergent
chemical synthesis and crystal structure of a
216
Christin Kossmann et al.
203 amino acid “covalent dimer” HIV-1 protease enzyme molecule. Angew Chem Int Ed
46:1667–1670
15. Durek T, Becker CF (2005) Protein semisynthesis: new proteins for functional and
structural studies. J Biomed Eng 22:153–172
16. Blanco-Canosa JB, Nardone B, Albericio F,
Dawson PE (2015) Chemical protein synthesis
using a second-generation N-acylurea linker
for the preparation of peptide-thioester precursors. J Am Chem Soc 13:7197–7209
17. Huang YC, Chen CC, Li SJ, Gao S, Shi J, Li
YM (2014) Facile synthesis of C-terminal peptide hydrazide and thioester of NY-ESO-1
(A39-A68) from an Fmoc-hydrazine 2-chlorotrityl chloride resin. Tetrahedron Lett
70:2951–2955
18. Muir TW, Sondhi D, Cole PA (1998)
Expressed protein ligation: a general method
for protein engineering. Proc Natl Acad Sci U S
A 95:6705–6710
19. Haase C, Rohde H, Seitz O (2008) Native
chemical ligation at valine. Angew Chem Int
Ed 47:6807–6810
20. Sato K, Kitakaze K, Nakamura T, Naruse N,
Aihara K, Shigenaga A, Otaka A (2015) The
total chemical synthesis of the monoglycosylated GM2 ganglioside activator using a novel
cysteine
surrogate.
Chem
Commun
51:9946–9948
21. Harpaz Z, Siman P, Kumar KA, Brik A (2010)
Protein synthesis assisted by native chemical
ligation
at
leucine.
Chembiochem
11:1232–1235
22. Han J, Luby-Phelps K, Das B, Shu X, Xia Y,
Mosteller RD, Broek D (1998) Role of substrates and products of PI 3-kinase in regulating activation of Rac-related guanosine
triphosphatases by Vav. Science 279:558–560
23. Wan Q, Danishefsky SJ (2007) Free-radicalbased, specific desulfurization of cysteine: a
powerful advance in the synthesis of
polypeptides and glycopolypeptides. Angew
Chem Int Ed 46:9248–9252
24. Pedersen SW, Moran GE, Sereikaitė V, Haugaard-Kedström LM, Strømgaard K (2016)
Importance of a conserved Lys/Arg residue
for ligand/PDZ domain interactions as examined by protein semisynthesis. Chembiochem
17:1936–1944
25. Eildal JN, Hultqvist G, Balle T, Stuhr-HansenN, Padrah S, Gianni S, Jemth P (2013) Probing
the role of backbone hydrogen bonds in protein–peptide interactions by amide-to-ester
mutations. J Am Chem Soc 135:12998–13007
26. Pedersen SW, Albertsen L, Moran GE,
Levesque B, Pedersen SB, Bartels L, Strømgaard K (2017) Site-specific phosphorylation
of PSD-95 PDZ domains reveals fine-tuned
regulation of protein-protein interactions.
ACS Chem Biol 12:2313–2323
27. Haj-Yahya M, Lashuel HA (2018) Protein
semisynthesis provides access to tau diseaseassociated post-translational modifications
(PTMs) and paves the way to deciphering the
tau PTM code in health and diseased states. J
Am Chem Soc 140:6611–6621
28. Chin JW, Cropp TA, Anderson JC,
Mukherji M, Zhang Z, Schultz PG (2003) An
expanded eukaryotic genetic code. Science
301:964–967
29. Eissler S, Kley M, B€achle D, Loidl G, Meier T,
Samson D (2017) Substitution determination
of Fmoc-substituted resins at different wavelengths. J Pept Sci 23:757–762
30. Applied Biosystems (1998) Cleavage, deprotection, and isolation of peptides after Fmoc
synthesis. Tech Bull 1–12
31. Schnölzer M, Alewood P, Jones A, Alewood D,
Kent SB (2007) In situ neutralization in
Boc-chemistry solid-phase peptide synthesis.
Int J Pept Res Ther 13:31–44
32. Jensen KJ (2013) Solid-phase peptide synthesis: an introduction. Peptide synthesis and
applications. Methods Mol Biol 1047:1–21
Chapter 13
Viral PDZ Binding Motifs Influence Cell Behavior Through
the Interaction with Cellular Proteins Containing PDZ
Domains
Carlos Castaño-Rodriguez, Jose M. Honrubia, Javier Gutiérrez-Álvarez,
Isabel Sola, and Luis Enjuanes
Abstract
Viruses have evolved to interact with their hosts. Some viruses such as human papilloma virus, dengue virus,
SARS-CoV, or influenza virus encode proteins including a PBM that interact with cellular proteins containing PDZ domains. There are more than 400 cellular protein isoforms with these domains in the human
genome, indicating that viral PBMs have a high potential to influence the behavior of the cell. In this review
we analyze the most relevant cellular processes known to be affected by viral PBM–cellular PDZ interactions
including the establishment of cell–cell interactions and cell polarity, the regulation of cell survival and
apoptosis and the activation of the immune system. Special attention has been provided to coronavirus
PBM conservation throughout evolution and to the role of the PBMs of human coronaviruses SARS-CoV
and MERS-CoV in pathogenesis.
Key words PDZ, PBM, Virus, Pathogenesis, Replication
1
Introduction
PDZ domains are protein–protein interaction sequences consisting
of 80–90 amino acids. They form a structure composed of an
antiparallel β barrel made of six β strands and two α helixes
(Fig. 1) [1]. PDZ is an acronym formed as the abbreviation of the
names of the three first proteins where this domain was identified:
postsynaptic density-95 (PSD-95), the drosophila tumor suppressor protein Dlg1 (discs large 1), and the tight junction protein
ZO-1 (zonula occludens 1) [2]. These domains can be found in
hundreds of proteins, both in eukaryotic cells and in bacteria
[3]. Up to 266 PDZ domains which are part of more than 400 different protein isoforms have been described in the human genome
[4, 5]. These proteins may include from one to thirteen PDZ
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_13, © Springer Science+Business Media, LLC, part of Springer Nature 2021
217
218
Carlos Castaño-Rodriguez et al.
Fig. 1 Structure of a PDZ domain: The secondary structure of a PDZ domain is
shown. Two alpha helixes (αA and αB) are shown in red and six beta sheets (βA,
βB, βC, βD, βE, and βF) are shown in yellow. (Figure modified from [1])
domains as is the case of PICK1 and MUPP1, respectively. In some
cases, two PDZ domains can be located in close proximity to each
other inside the same protein, such that they are arranged into a
PDZ tandem that may act as a single functional unit [4, 6].
PDZ domains are normally found together with other protein–
protein interaction domains in the same protein. For example,
proteins from the membrane-associated guanylate kinase
(MAGUK) include an SH3 module, multiple PDZ domains, and
a guanylate kinase domain which is inactive. The presence of different protein–protein interacting domains allows proteins containing
PDZ domains to bind many proteins at the same time and act as a
scaffold. This is why they participate in a wide variety of biological
processes such as cell polarity regulation, cell–cell interactions, cell
migration, proliferation and survival, intracellular transport, signal
transduction, or protein arrangement [5, 7].
PDZ domains mainly interact with a short stretch of amino acid
residues arranged in a specific manner called PDZ-binding-motifs
(PBMs). However, they may also interact with other PDZ domains
or even phospholipids [8, 9]. PBMs are specific sequences usually
located in the last amino acids of the protein. Although controversial, PDZ domains can be classified in three different groups,
according to the PBM sequence they interact with: Class I PDZ
domains recognize the motif X-S/T-X-Φ-COOH (where X could
be any amino acid and Φ is a hydrophobic amino acid (normally V,
I, or L), class II PDZs bind the sequence X-Φ-S-Φ-COOH, and
PBM-PDZ Interactions and Viral Pathogenesis
219
Fig. 2 PBM–PDZ binding structure: The binding of AF6 protein PDZ domain
(yellow) with Bcr PBM (blue) is shown. Dashed line (green) shows the
interactions between the PDZ and the PBM amino acids (Adapted from [88])
class III PDZs bind X-D/E-X-Φ-COOH. However, there are other
PDZ domains that do not fit into any of these groups
[10]. Although PBMs are located in the carboxy-terminus end of
proteins [1, 11, 12], internal PBMs have also been described and
could be more common than originally thought [13]. Interestingly,
some cellular proteins such as NHERF1 have a PDZ domain and a
PBM in the same protein, which contributes to regulate their
availability to interact with other proteins [14].
Structurally, PBMs bind PDZ domains in a process called
β-augmentation [15], in which the PBM acts as a new β chain
that binds βB chain from the PDZ domains (Fig. 2). Carboxy
moiety of the last amino acid of the PBM interacts through hydrogen bridges with the amide side-chain of the residues in loop βA-βB
of the PDZ, which is why they have great influence over the
specificity of the PDZ for the PBM. However, an increasing
amount of evidence indicates that PBM–PDZ interactions require
more residues than the four amino acids of the PBM and those of
loop βA-βB of the PDZ, as some PDZ domains require additional
sequences beyond the domain in order to be functional. These
PDZs are termed “extended PDZ domains” [6]. In fact, given
the high variability of PDZ sequences both in length and in
sequence, PBM–PDZ interactions can be highly diverse, which is
why predictions of which PBM interact with a given PDZ have
failed to be accurate [4].
Viruses have developed several mechanisms to interact with the
host and use its machinery for their own benefit. One of these
mechanisms includes the interaction with cellular PDZ proteins
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Carlos Castaño-Rodriguez et al.
Table 1
Viral proteins with PBMs and the PDZ domains they interact with
FAMILY
VIRUS
VIRAL PROTEIN
CELLULAR TARGET
PBM
SEQUENCE
Adenoviridae
Human
adenovirus (Ad9)
E4-ORF1
Dlg1, MAGI-1, MUPP1,
PATJ, ZO-2 (77)
…FPSVKIATLV
HBV
Core Protein
GIPC (78), PTPN3 (79)
…RRSQSRESQC
(non-canonical)
Hepadnaviridae
HCV
…WISSECTTPC
NS4b
Scribble (20)
NS1
Dlg1, MAGI-1, MAGI-2,
MAGI-3, Scribble (54)
PDLIM2 (80)
…KMARTIESEV
HPV
E6
GAL/GOPC, Dlg1, Dlg4,
MAGI-1, MAGI-2, MAIG-3,
MUPP1, PATJ, PTPN3,
PTPN13, Scribble, TIP-1,
TIP-2/GIPC (16)
…SSRTRRETQL
RhPV
E7
(non-canonical)
Orthomyxoviridae
Papillomaviridae
Retroviridae
IAV
HTLV1
Tax
Env
TBEV
NS5
Par3 (81)
Dlg1 (82), Scribble (48),
Pro-IL-16 (83)
β1-Syntrophin, Dlg4, Lin-7,
TIP-1, TIP2/GIPC, TIP-40
(19)
MAGI-3 (84), Erbin (85),
MAGI-1 (86)
Dlg1(87)
Scribble (61)
RIMS2, ZO-1 (38)
CASK, GIPC, ZO-2,
GRIP2,
Pro-IL-16 (21)
ZO-1 (38)
…DIVCPSCASRV
…SEKHFRETEV
…YSLINPESSL
…STHEMYYSTA…
(internal)
…WELRLESSII
…NESDPEGALW
DENV
NS5
WNV
NS5
HtrA2, OMP25, CLIM1,
ZO-2, PTPN13, PDLIM4,
PDZD2, GRIP2, Scribble,
and others (21)
…DTIVVEDTVL
Rhabdoviridae
RABV
G
Dlg2, MAST2, MUPP1,
PTPN4 (55)
…SHKSGGETRL
Poxviridae
Vaccinia virus
F11
F11 (24)
E
3a
7b
E
5
M
PALS1 (50), Syntenin (23)
GRIP11, APBA11
ND
Syntenin1
ND
ND
E
ND
3a
8
ND
ND
…LSLSNLDFRL
…SSEGVPDLLV
…EPTTTTSVPL
…QDLEEPCTKV
…KPPLPPDEWF
…HIIAPSSLIV
…ADIELALLRA
…SSEGVPDLLV
…EPTTTTSVPL
…HDVRVVLDFI
Flaviviridae
SARS-CoV
Coronaviridae
MERS-CoV
SARS-CoV-2
ND stands for nondetermined
a
C. Castaño-Rodriguez, E Bailly, P. Zimmermann, JP Borg, L. Enjuanes 2020, unpublished results
PBM-PDZ Interactions and Viral Pathogenesis
221
through viral proteins including a PBM (Table 1). Given the functional versatility of proteins including PDZ domains, viruses deregulate a wide range of cellular functions through their PBMs
modulating viral replication and dissemination, and contributing
to viral pathogenesis. In some cases, viral PBMs interact with
cellular PDZs contributing to their degradation or inactivation
and in others, they cause their activation or change their function
by subcellular relocalization [16, 17]. The first viral PBMs were
identified two decades ago in viral oncoproteins, like human papillomavirus (HPV) E6 protein, or human T-cell lymphotropic virus
type I (HTLV-1) tax protein [18, 19] or, more recently, Hepatitis C
virus (HCV) NS4b protein [20]. Furthermore, viral PBMs have
also been described in proteins from nononcogenic viruses, such as
proteins NS1 from influenza virus, NS5 from Tick-borne Encephalitis Virus (TBEV), or SARS-CoV E protein [21–23].
The diversity among the viral PBMs could be similar to that of
the cellular ones as some internal viral PBMs or viral proteins
containing both a PDZ domain and a PBM have been identified.
As an example, TBEV NS5 protein has two PBMs: one located in its
carboxy terminus end, similarly to its homolog proteins from other
flaviviruses such as Dengue Virus (DENV) or West Nile Virus
(WNV), and an internal PBM in its MTase domain [21]. Also,
F11 protein of vaccinia virus has both a PBM and a PDZ domain,
and the two of them are effectively coordinated to promote viral
dissemination [24].
In this chapter we will review how viral PBMs target relevant
cellular processes governed by cellular proteins including PDZ
domains: cell–cell junctions, polarity, and survival/apoptosis.
Also, the influence of these viral PBMs on the host immune system
contributing to viral pathogenicity will be discussed. In the last part
of the review the focus will be set on the PBMs in CoVs proteins,
their conservation through evolution and how they influence viral
replication and pathogenicity.
2
Cellular Processes Targeted by Viral PBMs
Viruses have adopted many strategies throughout evolution to use
the cellular machinery for their own biological processes as well as
to counteract host defenses. The best-known cellular processes
affected by viral PBMs are cell–cell junction formation, cell polarity
establishment, the regulation of cellular proliferation/apoptosis
and of the immune system. For a clearer understanding of how
viral PBMs work, it is worthy to focus on each of these processes
one by one.
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Carlos Castaño-Rodriguez et al.
2.1 Cell–Cell
Junctions
Many cellular proteins including PDZ domains regulate the formation of membrane junctions which are located at points of cell–cell
contact. There are three types of cell–cell junctions: tight junctions
(TJ), adherens junctions (AJ), and desmosomes. Of the three, TJs
are the most targeted by viral PBMs and they have an important
role maintaining epithelial integrity by creating a barrier to diffusion of solutes across cellular membranes in order to maintain
homeostasis in tissues and organs. Structurally, they are formed by
transmembrane proteins which establish the contact between cells
through their extracellular domains also called tight junction proteins, and by the junctional plaque, which is the complex of intracellular proteins that act as adaptors mediating the interaction of
the cytoplasmic domains of the TJ proteins with the cytoskeleton,
leading to a “tight” interaction between both cells [25]. Cellular
proteins including PDZ domains such as MAGI-I, PATJ, or
MUPP1 are some of these adaptor proteins that are targeted by
the PBMs of different kinds of viruses [25]. These are some of the
most relevant examples.
Adenovirus type 9 (Ad9) is a human virus associated with
benign eye infections that may also cause mammary tumors in
experimental rats [26, 27]. Most human adenovirus infections
lead to carcinogenesis by the products of their genes E1A and
E1B, however Ad9 solely depends on the presence of a PBM in
protein E4-ORF1 to promote cell transformation [28]. This protein has a type I PBM in its carboxy terminal (-ATLVCOOH ) that
binds four cellular proteins including PDZ domains that are
involved in establishing TJs: MUPP1, ZO-2, MAGI-1, and PATJ
[29–32]. These interactions lead to the sequestration and consequent inactivation of the four proteins, contributing to the disruption of TJs, one of the hallmarks of carcinogenesis.
HPV E6 protein has a class I PBM (-ETQLCOOH). This protein
forms complexes with the cellular proteins including PDZs and
E6AP ubiquitin ligase promoting the proteasome-mediated degradation of most of the cellular PDZ proteins interacting with E6
PBM. This viral protein also targets many cellular proteins including PDZ domains that contribute to TJ formation as PATJ,
MUPP1, MAGI-1 [29, 32, 33] [34]. However, instead of relocalizing the cellular proteins as Ad9 E4-ORF1 did, HPV E6 promotes
TJ disruption by targeting these proteins for degradation leading to
tumor formation.
Influenza A virus (IAV) is a respiratory virus that affects birds
and mammals. It encodes protein NS1, which is a virulence factor,
whose main function is to counteract the innate immune system
antiviral mechanisms. This protein has a class I PBM in its carboxy
terminus with a sequence that changes in isolates from different
species: in highly pathogenic avian viruses the consensus sequence
is -ESEVCOOH , while in less pathogenic human viruses PBM
sequence is -RSKVCOOH . Each of these sequences have different
PBM-PDZ Interactions and Viral Pathogenesis
223
PDZ binding properties [22]. The avian virulent PBM binds PDZ
proteins Scribble and Dlg1 [22]. Both proteins promote the assembly and stability of TJs through their interaction with proteins that
are part of the junctional plaque [35, 36]. It was observed that in
the context of the infection of an IAV including an NS1 protein
with the -ESEV PBM, the viral PBM sequestered Dlg1 and Scribble
to perinuclear puncta through a PBM–PDZ interaction disrupting
TJs structurally and functionally [37]. However, its effect on virus
pathogenesis is still to be described.
Flaviviruses Dengue Virus (DV), West Nile Virus (WNV) and
Tick-Borne Encephalitis Virus (TBEV) interact with ZO-1 and
ZO-2 through their carboxy terminus PBM with an unknown
effect [21, 38].
2.2
Cell Polarity
Cell polarity is a phenomenon characterized by an asymmetrical
distribution of biomolecules within the cells such as lipids, proteins,
or an asymmetrical distribution of the cell itself by forming specific
membrane domains, enriching organelles or the cytoskeleton at
specific sites [39]. Cell polarity can be apicobasal, as in epithelial
cells located in a multicellular sheet, including an apical membrane
and a basolateral membrane; anterior–posterior, as in the case of
migrating cells and planar cell polarity, which is developed within
the plane of a given tissue. This phenomenon is required for the
development of the organism and to maintain homeostasis, which
is why a deregulation of cell polarity may lead to tumorigenesis,
several birth defects [40] or diseases [41, 42].
Many cellular proteins including PDZ domains are relevant in
regulating cell polarity [5]. Apical-basal cell polarity is disrupted by
viral PBMs through the interaction with at least one of this three
conserved protein complexes involving proteins with a PDZ
domain: the Apical Crumbs complex involving PATJ-PALS1CRUMBS [43]; the TJ Par Complex involving PAR3-PAR6aPKC [44] and the Lateral Scribble Complex located at more
basal location including Dlg1-Lgl-Scribble [45]. These are some
of the most relevant examples of viral PBMs targeting apical-basal
cell polarity.
Oncovirus such as adenovirus E4-ORF1 and HPV-16 E6 protein PBMs interact and relocalize or eliminate PATJ disrupting the
Apical Crumbs complex leading to the loss of apical-basal polarity,
which promotes cell transformation [32]. HPV E6 PBM also targets the Lateral Scribble Complex such as Scribble and Dlg1 and
promotes their degradation [18, 46]. The consequence of this
degradation in cell polarity has not been fully studied but it could
contribute to tumorigenesis, as there are cervical cancers in which
Dlg1 and Scribble show a reduced expression or are absent
[47]. Human T Cell Leukemia Virus 1 (HTLV-1) Tax protein
includes a PBM in its carboxy terminus (-ETEVCOOH ) which is
relevant for virus-induced leukemia. This PBM binds Scribble
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Carlos Castaño-Rodriguez et al.
leading to perturbations in the cytoskeleton and the loss of T-cell
polarization contributing to tumorigenesis [48, 49].
Cell polarity is also targeted by respiratory viruses such as IAV
and SARS-CoV. In the case of coronaviruses (CoVs), SARS-CoV E
protein, includes a PBM at its carboxy terminus (-DLLVCOOH) that
interacts with the PDZ domain of PALS1. It was observed that this
PBM relocalized PALS1 to the ERGIC in SARS-CoV infected cells,
altering its original localization and disrupting the Apical Crumbs
complex. This activity delayed the formation of TJs between epithelial cells and disrupted cell polarity [50]. IAV NS1 PBM targets
Dlg1 and Scribble as described above, causing the disruption of the
lateral Scribble complex although the physiological consequences
of this disruption have not been clearly established. Both SARSCoV and IAV cause an exacerbated immune response that leads to
lung edema and death in the most severe cases. Edema clearance in
lung epithelia requires proper apico-basal polarity. Therefore, it has
been hypothesized that the disruption of cell polarity of lung
epithelia caused by these viral PBMs could hinder edema clearance
which would lead to edema accumulation and the death of the host.
However, this is yet to be confirmed.
2.3 Cell Survival
and Apoptosis
Viruses inhibit cell apoptosis and enhance cell survival in order to
have a proper environment to replicate. In the case of oncoviruses,
altering cell–cell interactions and cell polarity is part of cell tumorigenesis. However, this is also achieved by their PBMs directly
inhibiting signaling pathways that induce apoptosis and enhancing
those that promote cell survival. Dlg1 and Scribble are well known
for their tumor suppressor activity. Both proteins induce an inhibitory effect on the PI3K-Akt pathway, which upon activation, promotes the phosphorylation of Akt that leads to the activation of
several cellular signaling cascades causing cell proliferation and
survival. Dlg1 interacts with the PBM of cellular protein PTEN
contributing to its activation. PTEN is a phosphatase that acts on
PIP3 leading to the inhibition of the PI3K-Akt pathway. Similarly,
Scribble inhibits this pathway by its interaction with the PBM of the
cellular phosphatase PHLPP [51], localizing PHLPP to the membrane where it exerts its inhibitory effect over the PI3K-Akt pathway. HTLV-1 Tax protein PBM interacts with both Dlg1 and
Scribble competing with PTEN and PHLPP, respectively, contributing to the activation of the PI3K-Akt pathway [52].
Similarly, HPV E6 protein interacts and promotes the degradation of MAGI-2 and MAGI-3 [53], two PDZ proteins which bind
PTEN through its own PBM. It has been hypothesized that HPV
would induce the degradation of MAGI-2 and MAGI-3 leading to
the deregulation of PTEN causing cell malignancy. However, this
effect is yet to be proven experimentally. There are many other
signaling pathways involving cellular PDZ proteins. In this cases,
E6 PBM affects cell proliferation and survival, as reviewed [16].
PBM-PDZ Interactions and Viral Pathogenesis
225
On the other hand, viruses that are highly pathogenic but do
not generate tumors, like Rabies (RABV) or IAV, also interfere with
cellular PDZs affecting cell survival and apoptosis, but with a
different effect. For instance, the interaction of highly pathogenic
avian IAV H7N1 NS1 protein PBM (-ESEVCOOH ) with Scribble
led to an increase in viral titers that was not observed in a virus
variant with a mutant PBM (-ESEACOOH ), which did not bind
Scribble. This binding blocked Scribble antiapoptotic function
relocalizing the protein into cytoplasmic puncta, suggesting that
the inhibition of apoptosis by this PBM was responsible for an
increase in virus titers [54]. RABV virus, a neurotropic virus that
causes severe encephalitis in mammals, includes a PBM in the
carboxy terminus end of its G protein. Infections by RABV variants
that promote neuronal death by apoptosis lead to the survival of the
host in contrast to those that promote cell survival, which are highly
virulent [55]. This difference is due to the changes in the sequence
of G protein PBM. Variants that include the PBM -QTRLCOOH
bind the PDZ domain of cellular Ser/Thr kinase MAST2 with
similar affinity as its natural binder, PTEN [56]. This leads to the
activation of Akt and therefore, cell survival. In contrast, attenuated
variants include the sequence -ETRLCOOH , showing an increased
PDZ binding ability, binding to MAST2 and Dlg2, MUPP1 and
PTPN4, a protein that upon activation, promotes neural apoptosis
overcoming the effect of the binding to MAST2 [55]. Furthermore,
a recent analysis of the sequence of the G protein of high morbidity
and low morbidity RABV variants has revealed that low morbidity
viruses lack a PBM in its carboxy terminus domain, further supporting the implications on pathogenesis on RABV G protein PBM
[57, 58].
2.4 Disruption
of the Immune System
Viral PBMs also subvert the host immune system by triggering or
inhibiting signaling pathways contributing to viral pathogenesis
either by suppressing the immune system or by triggering an exacerbated immune response which is deleterious to the host.
T cells are responsible for adaptative immune response of the
host. The interaction with antigen presenting cells (APC) through
their membrane T-cell receptor (TCR) triggers signaling pathways
that lead to T-cell activation. These pathways are regulated in both
APCs and T-cells by several factors including Dlg1 and Scribble
[59]. One of them, is the Akt pathway, targeted by HTLV-1 Tax
protein, discussed above, as it is also involved in the regulation of
cell proliferation and apoptosis. The interaction of Tax protein
PBM with these proteins inhibits T cell activation, contributing to
the depletion of the adaptative immune response and the survival of
the virus within T-cells.
Also, Flaviviruses and CoVs influence the immune system
through the interaction of their PBMs with cellular PDZs. IFN is
one of the main determinants of host anti-viral response [60] and is
targeted by proteins NS5 from the Flaviviridae family, inhibiting
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Carlos Castaño-Rodriguez et al.
interferon (IFN) signaling in infected cells. TBEV NS5 protein
includes a PBM at its carboxy-terminus and an internal PBM that
binds Scribble [61]. This interaction does not relocalize or inhibit
Scribble’s activity, it just targets NS5 protein to the membrane,
where Scribble is generally located, and contributes to the inhibition of interferon mediated JAK-STAT signaling [61]. However,
the exact mechanism governing this inhibition or the relevance of
this interaction in the context of the viral infection is still to be
elucidated.
Similarly, the PBM–PDZ dependent interaction of IAV NS1
protein with both Dlg1 and Scribble has recently been reported in
APCs [59]. Although the consequence of this interaction in vivo is
still to be elucidated, it suggests a role of IAV NS1 PBM in modifying the host immune response.
In the case of CoVs, our laboratory generated a collection of
recombinant SARS-CoVs with mutations affecting E protein PBM.
Their analysis in vivo showed that SARS-CoV variants missing E
protein PBM were attenuated, indicating that E protein PBM was a
virulence determinant. Then, we described that the interaction of
this viral PBM with the PDZ domains of cellular protein syntenin
during viral infection activated p38 MAPK. This interaction promoted the over expression of proinflammatory cytokines, causing
the death of the host due to a pathogenic immune response
[23]. These data indicated a clear role of PBM–PDZ interaction
in the lung pathology observed in SARS-CoV infected patients.
Both CoV and Flaviviruses infect cells of the immune system,
although in some cases the infection is nonproductive [62–
65]. How the pathology of these viruses is influenced by the
interaction of these viral PBMs with cellular PDZ proteins of
immune cells still needs to be addressed.
3
Relevance of CoVs Proteins Including a PBM
CoVs have several proteins including PBMs binding a set of cellular
PDZs with the potential of influencing cell behavior. Some of these
interactions are reviewed next.
The relevance of SARS-CoV E protein PBM was further supported by the observation that a SARS-CoV attenuated deletion
mutant lacking E protein (SARS-CoV-ΔE), after several passages in
cell culture and in vivo, reverted to a virulent phenotype by incorporating chimeric proteins including new PBMs, thus compensating for the loss of E protein PBM and reinforcing its relevant role in
virus virulence [66].
Also, SARS-CoV 3a protein has a PBM motif in its carboxy
terminus (-SVPLCOOH ). However, this PBM is not involved in
virus replication and pathogenesis, in contrast to E protein PBM,
when located in its native protein, implying that either there is a
hierarchy between both PBMs when they are located in their native
PBM-PDZ Interactions and Viral Pathogenesis
227
contexts [67] or that the PBM works in collaboration with an
additional motif present in other viral proteins.
Interestingly, when a SARS-CoV variant lacking proteins E and
3a simultaneously was generated, the virus was not viable. In fact,
we described that either E or 3a protein PBM was essential for virus
viability when one of the two proteins was deleted, indicating that
there was a complementation between both viral PBMs [67]. Given
the relevance of SARS-CoV E and 3a protein PBMs, the presence of
PBM motifs in proteins of other CoVs species was reviewed
(Table 2). To date, the PBMs of SARS-CoV E and 3a proteins are
the only CoV PBMs that have been studied in detail.
The relevance of PBMs in other CoV proteins in virus pathogenicity and replications has been determined with limited extent.
Recently in our laboratory, we have shown that MERS-CoV E
protein PBM is also a virulence factor, suggesting that this virus
could trigger a similar virulence mechanism than SARS-CoV
(FJ Gutiérrez-Álvarez, Enjuanes, 2020, unpublished results).
Also, the relevance in virus replication and pathogenesis of human
OC43-CoV E protein PBM has been recently proposed
[68]. Another example is FIPV 7b protein, that includes a PBM
that when modified, changes the subcellular localization of the
protein [69]. A new human highly pathogenic coronavirus,
named SARS-CoV-2 [70] emerged in Wuhan in December 2019
and to date it has spread to more than 200 countries with more
than 51.000.000 confirmed cases and causing 1.280.000 deaths
(data from WHO as to 13, November, 2020) leading to an unparalleled global crisis. This new pathogen was analyzed showing 79%
sequence identity to SARS-CoV [71]. Interestingly, this virus has a
3a and E proteins in which the PBM sequence is identical to that of
SARS-CoV, suggesting that these PBMs could be involved in virus
replication and pathogenesis triggering the same cellular pathways
that are activated by SARS-CoV E and 3a protein PBMs. Although
functional studies are required to confirm this hypothesis, SARSCoV-2 PBMs could be a promising target for potential antivirals.
Current theories state that CoVs were originated in bats and
then transmitted to birds generating two branches of CoVs that
were classified in four CoVs genera: Alphacoronavirus and Betacoronavirus, which are derived from bat CoVs and Gammacoronavirus
and Deltacoronavirus, which are derived from bird CoVs
[72]. Forty-three potential PBMs have been identified in
33 CoVs species.
It has been observed that there are more PBMs in CoVs from
genera derived from bat CoVs (Alphacoronavirus and Betacoronavirus) than in those derived from bird CoVs (Gammacoronavirus
and Deltacoronavirus). This suggests that, during CoV evolution,
PBMs were incorporated in CoV genomes earlier in genera Alphacoronavirus and Betacoronavirus. Furthermore, a PBM was identified in the carboxy terminus of the E protein of every analyzed CoV
Table 2
CoV proteins including a PBM
GENERA
β
γ
Human
E
…APVPAEVLNV
E strain FS772/70
E strain Purdue
3a strain FS772/70
3a strain Purdue
E
E
E
3a
7a strain BGF10, Insavc-1
7a strain K378
7b
E
3a
7b
E
5
M
E
3a
8
E
N
E
N
E
N
E
M
N
E
N
HE (only in strain F15)
N
E
5a
E
S
5a
E
5c
M
ORF9
N
E
5c
M
NS9
N
…AYNHDGALLV
…AYNPDGALLA
…AYAKLGLSTI
…IEEVNSHIVV
…DPLPSTVIDV
…AYNPDGALLV
…AYNPDEALLV
…IEEVNSHTVV
…CCHRLLVTLF
…CCYRLLVTLI
…KISQYQKSEL
…SSEGVPDLLV
…EPTTTTSVPL
…QDLEEPCTKV
…KPPLPPDEWF
…HIIAPSSLIV
…ADIELALLRA
…SSEGVPDLLV
…EPTTTTSVPL
…HDVRVVLDFI
…VIPSTLDDLI
…DDPYVEDSVA
…KPPVLDVDDV
…EPYTEDTSEI
…KPPVLDVDDV
…EPYTEDTSEI
…RLPLLEVDDI
…SGADTALLRI
…PDGLEDDSNV
…KPPVLDVDDV
…EPYTEDTSEI
…YFMVENGTRL
…PDGLEDDSNV
…LAYTPTQSLV
…FSNSVNSSLV
…LVCTPTQSLV
…PRNSKDGEYV
…CIGNDAYLGV
…KTETEKLYSV
…NRKAYGSDEV
…GTGDLEWSEA
…PRNSKDGEYV
…CIGNDAYLGV
…KTETEKLYSV
…NRKAYGSDEV
…GTGDLEWSEA
HOST
FIPV
Cat
HCoV-229E
NL63
α
Human
E
3a
7b
E
CARBOXY
TERMINUS
SEQUENCE
…AYNPDEAFLV
…IEEVNSHTVV
…KINQHHKTEL
…DPFPKRVIDF
VIRUS
TGEV
Swine
PEDV
PRCV
Swine
Swine
CCoV
Dog
SARS-CoV
Human
MERS-CoV
Human
SARS-CoV-2
Human
HKU1
Human
OC-43
Human
HEV
Swine
MHV
Mouse
HCoV-4408
Bovine
BCV
Bovine
IBV
Bird
TCoV
Bird
SW1
Beluga whale
HKU22
Bottlenose
dolphin
VIRAL PROTEIN
PBM-PDZ Interactions and Viral Pathogenesis
229
Table 2
(Continued)
GENERA
δ
VIRUS
HOST
HKU15
Swine
ALCoV/GX/
F230/06
Leopard
HKU16
Bird
HKU17
Bird
HKU18
Bird
HKU19
HKU20
Bird
Bird
HKU21
Bird
VIRAL PROTEIN
5c
M
NS9
N
NS7
NS6
N
E
N
E
N
E
NS7a
NS7b
CARBOXY
TERMINUS
SEQUENCE
…CIGNDAYLGV
…KTETEKLYSV
…NRKAYGSDEV
…AFEIKQESAA
…RVWLILASWL
…SLQVILEEEI
…EIKRDEESTA
…AFEIKQESAA
…NAFEFKSSDA
…HQFPRNSFSV
…VKRKSLIDSA
…SDADISSDDA
from genera Alphacoronavirus and Betacoronavirus. However, this
was not the case in CoVs evolved from birds with the exception of E
protein from bottlenose dolphin CoV HKU22. Interestingly,
SARS-CoV, MERS-CoV, and SARS-CoV-2, the three human
CoVs that are highly pathogenic, have three proteins including a
PBM in their carboxy-terminus: proteins E, 3a and 7b in SARSCoV, proteins E, 5 and M in MERS-CoV and proteins E, 3a and
8 in SARS-CoV-2. In addition, MERS-CoV is the only CoV in
which a PBM has been described in M protein, a structural CoV
protein essential for viral assembly [73]. Remarkably, MERS-CoV
proteins E and 5 are homologs of SARS-CoV and SARS-CoV2 proteins E and 3a, respectively, suggesting that they may play a
similar role in MERS-CoV. Furthermore, SARS-CoV and MERSCoV, as most Betacoronaviruses, were originated in bats and then
transmitted to humans through intermediate hosts: civet cats in the
case of SARS-CoV, and camels in the case of MERS-CoV. GenBank
data from genomes of hundreds of SARS-CoVs and MERS-CoVs
variants isolated from bats, civets, camels and humans were analyzed showing that the PBMs from SARS-CoV E and 3a proteins
were mostly conserved in bats, civets and humans similarly to the
PBMs of MERS-CoV proteins E, 5 that were also mostly conserved
in bats, camels and humans (Table 3, adapted from [67]).
Interestingly, the mutations that affected the PBM core
sequences introduced a different PBM, likely functional in the
corresponding animal context. This phylogenetic conservation
reinforces the relevance of viral PBMs in CoVs opening the possibility that they could be involved in CoV adaptation to the host.
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Carlos Castaño-Rodriguez et al.
Table 3
PBMs conserved in MERS-CoV and MERS-CoV isolated from animals
VIRAL
PROTEIN
SARS-CoV E
PROTEIN
MERS-CoV E
PROTEIN
SARS-CoV
3a PROTEIN
MERS-CoV 5
PROTEIN
VIRUS
HOST
Nº ISOLATES
PBM
SARS-CoV
SARS-CoV
SARS-CoV Like
>100
>20
1
…NLNSSEGVPDDLV
…NLNSSEGVPDDLV
…NLNSSVGVPDDLV
1
…NLNSSDCVPDDLV
SARS-CoV Like
MERS-CoV
MERS-CoV
MERS-CoV-Like
MERS-CoV-Like
MERS-CoV-Like HKU5
MERS-CoV-Like-HKU4
SARS-CoV
SARS-CoV
SARS-CoV
SARS-CoV-Like
Zaria Bat-CoV
MERS-CoV
Human
Civet Cats
Bats R. pusillus
Bats R.
ferrumequinum
Bats R. blasii
Human
Dromedary
Bats N. capensis
Bats V. superans
Bats Pipistrellus
Bats T. pachypus
Human
Human
Civet Cats
Bats R. sinicus
H. commersoni
Human
1
98
17
1
1
6
7
166
3
4
1
1
97
…SLNSSQEVPEFLV
…QDSKPPLPPDEWV
…QDSKPPLPPDEWV
…QESKPPLPPEEWV
…QESKPPLPPDEWV
…QESHPPYPPEDWV
…QENRPPFPPEDWV
…IYDEPTTTTSVPL
…IYDEPMTTTSVPL
…IYDEPTTTTSVPL
…IYDEPMTTTSVPL
…IYDEPPTTTSVPL
…VPLHIIAPSLIV
MERS-CoV
Dromedary
17
…VPLHIIAPSLIV
MERS-CoV-Like
MERS-CoV-Like
MERS-CoV-Like HKU5
MERS-CoV-Like-HKU4
Bats N. capensis
Bats V. superans
Bats Pipistrellus
Bats T. pachypus
1
1
6
7
…VPLHIIAPVLSV
…VPLHIIAPVLSV
…VPLHIIAPVLTV
…VPLHIIAPKLYV
SARS-CoV Like
SARS-CoV 7b protein does not seem to have any role in virus
pathogenesis or replication as a deletion mutant simultaneously
missing genes 6, 7a, 7b, 8a, 8b and 9b (SARS-CoV Δ6-9b) showed
similar virus replication rates and caused pathogenesis similar to the
wild type virus [74]. However, SARS-CoV 7b protein PBM is not
present in SARS-CoV variants isolated from bats, in contrast to
viruses isolated from civet cats and humans, indicating that 7b
protein PBM could be involved in virus adaptation, similarly to
the PBMs of E and 3a proteins [75].
To further explore the relevance of CoV PBMs and their
involvement in virus replication and pathogenesis, the cellular
PDZ binding partners of these viral PBMs need to be identified.
To this end, a yeast-two hybrid assay was performed in which the
highly pathogenic human CoV, SARS-CoV and MERS-CoV, PBMs
were used as bait and the 266 human cellular PDZ domains were
used as prey (Fig. 3). It was observed that SARS-CoV and MERSCoV E protein PBMs bind cellular PDZ proteins involved in the
regulation of NfκB pathway, one of the hallmarks of SARS-CoV
pathogenesis [76], potentially contributing to virus virulence
PBM-PDZ Interactions and Viral Pathogenesis
231
Fig. 3 Y2H assay to study the interaction between viral PBMs and cellular PDZ domains: Yeast from strain
AH109 were transformed with a pGBT9 plasmid expressing GAL4 DNA binding domain fused to the last
15 amino acids of the proteins E and 3a of SARS-CoV and E and 5 of MERS-CoV. Yeasts from the strain Y187
were transformed by a library of every human PDZ domain fused to GAL4 activating domain [89]. Both strains
were mated. If an interaction between a viral PBM and a cellular PDZ takes place, then GAL4 is reconstituted,
activating a reporter gene, and therefore allowing for the identification of the partners involved in the
interaction
(C. Castaño-Rodriguez, P. Zimmermann, JP Borg, L. Enjuanes
2020, unpublished results).
4
Concluding Remarks
Viruses have adapted throughout evolution to interact with their
hosts. The introduction of PBMs that interact with cellular PDZ
proteins is one of these modifications. Decades of scientific studies
have revealed that there are many cellular processes affected by
these PBMs. It has also been observed that different viral PBM
sequences interact with a different set of cellular PDZ proteins even
if, in some cases, they share some specific PDZ targets (Table 1). To
date, the mechanisms used by viral oncoproteins including a PBM
influencing cell malignancy have been the most studied as reviewed
[16]. However, it is worth noting that both oncovirus and nononcovirus PBMs target identical cellular PDZ proteins that are
involved in the same cellular processes, but the effect of the interaction is quite different. For example, the interaction of oncovirusPBMs with Dlg1 or Scribble proteins trigger signaling pathways
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Carlos Castaño-Rodriguez et al.
leading to tumorigenesis and when they are targeted by Flavivirus
or IAV protein PBMs they modulate the same signaling pathways
affecting the same cellular processes but the result of the infection is
quite different [52, 61]. This is probably due to the specific cellular
context of each infection, as each of these viruses infects different
cell types, and the effect in each viral infection is unique.
Interestingly, one of the differences between viral and cellular
PBMs is that as viruses evolve faster than the cellular genomes, viral
PBMs may change throughout evolution to adapt to new hosts or
contexts, as has been observed by the incorporation of new PBMs
in SARS-CoV genome when E protein PBM was deleted [66] or
with the changes in PBM core sequences in some SARS-CoV and
MERS-CoV variants isolated from bats [67]. In fact, more than
40 different PBMs were identified in more than 33 CoV genomes
isolated from different hosts, reinforcing the relevance of viral PBMs
in virus adaptation and suggesting that CoVs could be a good model
to study the relevance of viral PBMs in virus evolution. Furthermore,
viral PBMs seem promising targets for antiviral therapy, as small
peptides blocking the interaction of viral PBMs with the cellular
PDZs could be used as antivirals to block virus pathogenicity.
There is still much to be learned about how viral PBMs work.
The field of nononcovirus PBMs is essentially unexplored with the
exception of CoVs. To gain a better understanding on how viral
PBMs influence viral infections, the mechanisms of replication and
pathogenesis induced by the PBMs of nononcogenic viruses such as
CoV, IAV, DENV or RABV need to be further studied. For the
moment, we have shown that the study of the cellular mechanisms
disrupted by human CoV PBMs are highly relevant for a better
understanding of virus–host interaction. Hopefully, this will open
the way to study viral PBMs of other highly pathogenic viruses in
more detail.
References
1. Gerek ZN, Keskin O, Ozkan SB (2009) Identification of specificity and promiscuity of PDZ
domain interactions through their dynamic
behavior. Proteins 77:796–811
2. Kennedy MB (1995) Origin of PDZ (DHR,
GLGF) domains. Trends Biochem Sci 20:350
3. Ponting CP (1997) Evidence for PDZ domains
in bacteria, yeast, and plants. Protein Sci
6:464–468
4. Luck K, Charbonnier S, Trave G (2012) The
emerging contribution of sequence context to
the specificity of protein interactions mediated
by PDZ domains. FEBS Lett 586:2648–2661
5. Nourry C, Grant SG, Borg JP (2003) PDZ
domain proteins: plug and play! Sci STKE
2003:RE7
6. Ye F, Zhang M (2013) Structures and target
recognition modes of PDZ domains: recurring
themes and emerging pictures. Biochem J
455:1–14
7. Subbaiah VK, Kranjec C, Thomas M, Banks L
(2011) PDZ domains: the building blocks regulating
tumorigenesis.
Biochem
J
439:195–205
8. Gallardo R, Ivarsson Y, Schymkowitz J,
Rousseau F, Zimmermann P (2010) Structural
diversity of PDZ-lipid interactions. Chembiochem 11:456–467
PBM-PDZ Interactions and Viral Pathogenesis
9. Hillier BJ, Christopherson KS, Prehoda KE,
Bredt DS, Lim WA (1999) Unexpected
modes of PDZ domain scaffolding revealed by
structure of nNOS-syntrophin complex. Science 284:812–815
10. Harris BZ, Lim WA (2001) Mechanism and
role of PDZ domains in signaling complex
assembly. J Cell Sci 114:3219–3231
11. Hung AY, Sheng M (2002) PDZ domains:
structural modules for protein complex assembly. J Biol Chem 277:5699–5702
12. Munz M, Hein J, Biggin PC (2012) The role of
flexibility and conformational selection in the
binding promiscuity of PDZ domains. PLoS
Comput Biol 8:e1002749
13. Lenfant N, Polanowska J, Bamps S, Omi S,
Borg JP, Reboul J (2010) A genome-wide
study of PDZ-domain interactions in
C. elegans reveals a high frequency of
non-canonical binding. BMC Genomics
11:671
14. Bhattacharya S, Dai Z, Li J, Baxter S, Callaway
DJ, Cowburn D, Bu Z (2010) A conformational switch in the scaffolding protein
NHERF1 controls autoinhibition and complex
formation. J Biol Chem 285:9981–9994
15. Remaut H, Waksman G (2006) Proteinprotein interaction through beta-strand addition. Trends Biochem Sci 31:436–444
16. James CD, Roberts S (2016) Viral interactions
with PDZ domain-containing proteins-an
oncogenic trait? Pathogens 5:8
17. Javier RT, Rice AP (2011) Emerging theme:
cellular PDZ proteins as common targets of
pathogenic viruses. J Virol 85:11544–11556
18. Kiyono T, Hiraiwa A, Fujita M, Hayashi Y,
Akiyama T, Ishibashi M (1997) Binding of
high-risk human papillomavirus E6 oncoproteins to the human homologue of the Drosophila discs large tumor suppressor protein.
Proc Natl Acad Sci U S A 94:11612–11616
19. Rousset R, Fabre S, Desbois C, Bantignies F,
Jalinot P (1998) The C-terminus of the
HTLV-1 tax oncoprotein mediates interaction
with the PDZ domain of cellular proteins.
Oncogene 16:643–654
20. Hu B, Li S, Zhang Z, Xie S, Hu Y, Huang X,
Zheng Y (2016) HCV NS4B targets scribble
for proteasome-mediated degradation to facilitate cell transformation. Tumour Biol
37:12387–12396
21. Melik W, Ellencrona K, Wigerius M,
Hedstrom C, Elvang A, Johansson M (2012)
Two PDZ binding motifs within NS5 have
roles in Tick-borne encephalitis virus replication. Virus Res 169:54–62
233
22. Obenauer JC, Denson J, Mehta PK, Su X,
Mukatira S, Finkelstein DB, Xu X, Wang J,
Ma J, Fan Y, Rakestraw KM, Webster RG,
Hoffmann E, Krauss S, Zheng J, Zhang Z,
Naeve CW (2006) Large-scale sequence analysis of avian influenza isolates. Science
311:1576–1580
23. Jimenez-Guardeño JM, Nieto-Torres JL,
DeDiego ML, Regla-Nava JA, FernandezDelgado R, Castaño-Rodriguez C, Enjuanes
L (2014) The PDZ-binding motif of severe
acute respiratory syndrome coronavirus envelope protein is a determinant of viral pathogenesis. PLoS Pathog 10:e1004320
24. Handa Y, Durkin CH, Dodding MP, Way M
(2013) Vaccinia virus F11 promotes viral
spread by acting as a PDZ-containing scaffolding protein to bind myosin-9A and inhibit
RhoA signaling. Cell Host Microbe 14:51–62
25. Zihni C, Mills C, Matter K, Balda MS (2016)
Tight junctions: from simple barriers to multifunctional molecular gates. Nat Rev Mol Cell
Biol 17:564–580
26. Javier R, Raska K Jr, Macdonald GJ, Shenk T
(1991) Human adenovirus type 9-induced rat
mammary tumors. J Virol 65:3192–3202
27. Thomas DL, Schaack J, Vogel H, Javier R
(2001) Several E4 region functions influence
mammary tumorigenesis by human adenovirus
type 9. J Virol 75:557–568
28. Javier R, Raska K Jr, Shenk T (1992) Requirement for the adenovirus type 9 E4 region in
production of mammary tumors. Science
257:1267–1271
29. Lee SS, Glaunsinger B, Mantovani F, Banks L,
Javier RT (2000) Multi-PDZ domain protein
MUPP1 is a cellular target for both adenovirus
E4-ORF1 and high-risk papillomavirus type
18 E6 oncoproteins. J Virol 74:9680–9693
30. Glaunsinger BA, Weiss RS, Lee SS, Javier R
(2001) Link of the unique oncogenic properties of adenovirus type 9 E4-ORF1 to a select
interaction with the candidate tumor suppressor protein ZO-2. EMBO J 20:5578–5586
31. Glaunsinger BA, Lee SS, Thomas M, Banks L,
Javier R (2000) Interactions of the
PDZ-protein MAGI-1 with adenovirus
E4-ORF1 and high-risk papillomavirus E6
oncoproteins. Oncogene 19:5270–5280
32. Latorre IJ, Roh MH, Frese KK, Weiss RS,
Margolis B, Javier RT (2005) Viral
oncoprotein-induced mislocalization of select
PDZ proteins disrupts tight junctions and
causes polarity defects in epithelial cells. J Cell
Sci 118:4283–4293
33. Storrs CH, Silverstein SJ (2007) PATJ, a tight
junction-associated PDZ protein, is a novel
234
Carlos Castaño-Rodriguez et al.
degradation target of high-risk human papillomavirus E6 and the alternatively spliced isoform 18 E6. J Virol 81:4080–4090
34. Kranjec C, Banks L (2011) A systematic analysis of human papillomavirus (HPV) E6 PDZ
substrates identifies MAGI-1 as a major target
of HPV type 16 (HPV-16) and HPV-18 whose
loss accompanies disruption of tight junctions.
J Virol 85:1757–1764
35. Ivanov AI, Young C, Den Beste K, Capaldo
CT, Humbert PO, Brennwald P, Parkos CA,
Nusrat A (2010) Tumor suppressor scribble
regulates assembly of tight junctions in the
intestinal
epithelium.
Am
J
Pathol
176:134–145
36. Stucke VM, Timmerman E, Vandekerckhove J,
Gevaert K, Hall A (2007) The MAGUK protein MPP7 binds to the polarity protein hDlg1
and facilitates epithelial tight junction formation. Mol Biol Cell 18:1744–1755
37. Golebiewski L, Liu H, Javier RT, Rice AP
(2011) The avian influenza virus NS1 ESEV
PDZ binding motif associates with Dlg1 and
scribble to disrupt cellular tight junctions. J
Virol 85:10639–10648
38. Ellencrona K, Syed A, Johansson M (2009)
Flavivirus NS5 associates with host-cell proteins zonula occludens-1 (ZO-1) and regulating synaptic membrane exocytosis-2 (RIMS2)
via an internal PDZ binding mechanism. Biol
Chem 390:319–323
39. Ebnet K, Kummer D, Steinbacher T, Singh A,
Nakayama M, Matis M (2018) Regulation of
cell polarity by cell adhesion receptors. Semin
Cell Dev Biol 81:2–12
40. Butler MT, Wallingford JB (2017) Planar cell
polarity in development and disease. Nat Rev
Mol Cell Biol 18:375–388
41. Schneeberger K, Roth S, Nieuwenhuis EES,
Middendorp S (2018) Intestinal epithelial cell
polarity defects in disease: lessons from microvillus inclusion disease. Dis Model Mech 11:
dmm031088.
https://doi.org/10.1242/
dmm.031088
42. Fischer E, Legue E, Doyen A, Nato F, Nicolas
JF, Torres V, Yaniv M, Pontoglio M (2006)
Defective planar cell polarity in polycystic kidney disease. Nat Genet 38:21–23
43. Medina E, Lemmers C, Lane-Guermonprez L,
Le Bivic A (2002) Role of the Crumbs complex
in the regulation of junction formation in Drosophila and mammalian epithelial cells. Biol
Cell 94:305–313
44. Yamanaka T, Horikoshi Y, Suzuki A,
Sugiyama Y, Kitamura K, Maniwa R, Nagai Y,
Yamashita A, Hirose T, Ishikawa H, Ohno S
(2001) PAR-6 regulates aPKC activity in a
novel way and mediates cell-cell contactinduced formation of the epithelial junctional
complex. Genes Cells 6:721–731
45. Yamanaka T, Ohno S (2008) Role of Lgl/Dlg/
scribble in the regulation of epithelial junction,
polarity
and
growth.
Front
Biosci
13:6693–6707
46. Nakagawa S, Huibregtse JM (2000) Human
scribble (Vartul) is targeted for ubiquitinmediated degradation by the high-risk papillomavirus E6 proteins and the E6AP ubiquitinprotein ligase. Mol Cell Biol 20:8244–8253
47. Stephens R, Lim K, Portela M, Kvansakul M,
Humbert PO, Richardson HE (2018) The
scribble cell polarity module in the regulation
of cell signaling in tissue development and
tumorigenesis. J Mol Biol 430:3585–3612
48. Arpin-Andre C, Mesnard JM (2007) The PDZ
domain-binding motif of the human T cell leukemia virus type 1 tax protein induces mislocalization of the tumor suppressor hScrib in T
cells. J Biol Chem 282:33132–33141
49. Peres E, Blin J, Ricci EP, Artesi M, Hahaut V,
Van den Broeke A, Corbin A, Gazzolo L,
Ratner L, Jalinot P, Duc Dodon M (2018)
PDZ domain-binding motif of tax sustains
T-cell proliferation in HTLV-1-infected humanized mice. PLoS Pathog 14:e1006933
50. Teoh KT, Siu YL, Chan WL, Schluter MA, Liu
CJ, Peiris JS, Bruzzone R, Margolis B, Nal B
(2010) The SARS coronavirus E protein interacts with PALS1 and alters tight junction formation and epithelial morphogenesis. Mol Biol
Cell 21:3838–3852
51. Li X, Yang H, Liu J, Schmidt MD, Gao T
(2011) Scribble-mediated membrane targeting
of PHLPP1 is required for its negative regulation of Akt. EMBO Rep 12:818–824
52. Cherian MA, Baydoun HH, Al-Saleem J,
Shkriabai N, Kvaratskhelia M, Green P, Ratner
L (2015) Akt pathway activation by human
T-cell leukemia virus type 1 tax oncoprotein. J
Biol Chem 290:26270–26281
53. Thomas M, Laura R, Hepner K, Guccione E,
Sawyers C, Lasky L, Banks L (2002) Oncogenic human papillomavirus E6 proteins target
the MAGI-2 and MAGI-3 proteins for degradation. Oncogene 21:5088–5096
54. Liu H, Golebiewski L, Dow EC, Krug RM,
Javier RT, Rice AP (2010) The ESEV
PDZ-binding motif of the avian influenza A
virus NS1 protein protects infected cells from
apoptosis by directly targeting scribble. J Virol
84:11164–11174
55. Prehaud C, Wolff N, Terrien E, Lafage M,
Megret F, Babault N, Cordier F, Tan GS,
Maitrepierre E, Menager P, Chopy D,
PBM-PDZ Interactions and Viral Pathogenesis
Hoos S, England P, Delepierre M, Schnell MJ,
Buc H, Lafon M (2010) Attenuation of rabies
virulence: takeover by the cytoplasmic domain
of its envelope protein. Sci Signal 3:ra5
56. Terrien E, Chaffotte A, Lafage M, Khan Z,
Prehaud C, Cordier F, Simenel C,
Delepierre M, Buc H, Lafon M, Wolff N
(2012) Interference with the PTEN-MAST2
interaction by a viral protein leads to cellular
relocalization of PTEN. Sci Signal 5:ra58
57. Seo W, Prehaud C, Khan Z, Sabeta C, Lafon M
(2017) Investigation of rabies virus glycoprotein carboxyl terminus as an in vitro predictive
tool of neurovirulence. A 3R approach.
Microbes Infect 19:476–484
58. Seo W, Servat A, Cliquet F, Akinbowale J,
Prehaud C, Lafon M, Sabeta C (2017) Comparison of G protein sequences of
South African street rabies viruses showing distinct progression of the disease in a mouse
model of experimental rabies. Microbes Infect
19:485–491
59. Barreda D, Sanchez-Galindo M, Lopez-FloresJ, Nava-Castro KE, Bobadilla K, SalgadoAguayo A, Santos-Mendoza T (2018) PDZ
proteins are expressed and regulated in
antigen-presenting cells and are targets of influenza A virus. J Leukoc Biol 103:731–738
60. Platanias LC (2005) Mechanisms of type-Iand type-II-interferon-mediated signalling.
Nat Rev Immunol 5:375–386
61. Werme K, Wigerius M, Johansson M (2008)
Tick-borne encephalitis virus NS5 associates
with membrane protein scribble and impairs
interferon-stimulated JAK-STAT signalling.
Cell Microbiol 10:696–712
62. King NJ, Getts DR, Getts MT, Rana S,
Shrestha B, Kesson AM (2007) Immunopathology of flavivirus infections. Immunol Cell
Biol 85:33–42
63. Fernandez-Garcia MD, Mazzon M, Jacobs M,
Amara A (2009) Pathogenesis of flavivirus
infections: using and abusing the host cell.
Cell Host Microbe 5:318–328
64. Yilla M, Harcourt BH, Hickman CJ,
McGrew M, Tamin A, Goldsmith CS, Bellini
WJ, Anderson LJ (2005) SARS-coronavirus
replication in human peripheral monocytes/
macrophages. Virus Res 107:93–101
65. Zhou J, Chu H, Li C, Wong BH, Cheng ZS,
Poon VK, Sun T, Lau CC, Wong KK, Chan JY,
Chan JF, To KK, Chan KH, Zheng BJ, Yuen
KY (2014) Active replication of Middle East
respiratory syndrome coronavirus and aberrant
induction of inflammatory cytokines and chemokines in human macrophages: implications
for pathogenesis. J Infect Dis 209:1331–1342
235
66. Jimenez-Guardeno JM, Regla-Nava JA, NietoTorres JL, DeDiego ML, Castano-RodriguezC, Fernandez-Delgado R, Perlman S, Enjuanes
L (2015) Identification of the mechanisms
causing reversion to virulence in an attenuated
SARS-CoV for the design of a genetically stable
vaccine. PLoS Pathog 11:e1005215
67. Castaño-Rodriguez C, Honrubia JM, Gutiérrez-Álvarez J, DeDiego ML, Nieto-Torres JL,
Jimenez-Guardeño JM, Regla-Nava JA,
Fernandez-Delgado R, Verdia-Báguena C,
Queralt-Martı́n M, Kochan G, Perlman S,
Aguilella VM, Sola I, Enjuanes L (2018) Role
of severe acute respiratory syndrome coronavirus viroporins E, 3a, and 8a in replication and
pathogenesis. mBio 9:e2325–e2317
68. Stodola JK, Dubois G, Le Coupanec A,
Desforges M, Talbot PJ (2018) The OC43
human coronavirus envelope protein is critical
for infectious virus production and propagation in neuronal cells and is a determinant of
neurovirulence and CNS pathology. Virology
515:134–149
69. Florek D, Ehmann R, Kristen-Burmann C,
Lemmermeyer T, Lochnit G, Ziebuhr J, Thiel
HJ, Tekes G (2017) Identification and characterization of a Golgi retention signal in feline
coronavirus accessory protein 7b. J Gen Virol
98:2017–2029
70. Gorbalenya AE, Baker SC, Baric RS, de Groot
RJ, Drosten C, Gulyaeva AA, Haagmans BL,
Lauber C, Leontovich AM, Neuman BW,
Penzar D, Perlman S, LLM P, Samborskiy
DV, Sidorov IA, Sola I, Ziebuhr J, Coronaviridae Study Group of the International Committee on Taxonomy of V (2020) The species
severe acute respiratory syndrome-related
coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5:536–544
71. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H,
Wang W, Song H, Huang B, Zhu N, Bi Y,
Ma X, Zhan F, Wang L, Hu T, Zhou H,
Hu Z, Zhou W, Zhao L, Chen J, Meng Y,
Wang J, Lin Y, Yuan J, Xie Z, Ma J, Liu WJ,
Wang D, Xu W, Holmes EC, Gao GF, Wu G,
Chen W, Shi W, Tan W (2020) Genomic characterisation and epidemiology of 2019 novel
coronavirus: implications for virus origins and
receptor binding. Lancet 395:565–574
72. Lau SK, Woo PC, Li KS, Tsang AK, Fan RY,
Luk HK, Cai JP, Chan KH, Zheng BJ,
Wang M, Yuen KY (2015) Discovery of a
novel coronavirus, China Rattus coronavirus
HKU24, from Norway rats supports the
murine origin of Betacoronavirus 1 and has
implications for the ancestor of Betacoronavirus lineage A. J Virol 89:3076–3092
236
Carlos Castaño-Rodriguez et al.
73. Neuman BW, Kiss G, Kunding AH, Bhella D,
Baksh MF, Connelly S, Droese B, Klaus JP,
Makino S, Sawicki SG, Siddell SG, Stamou
DG, Wilson IA, Kuhn P, Buchmeier MJ
(2011) A structural analysis of M protein in
coronavirus assembly and morphology. J Struct
Biol 174:11–22
74. DeDiego ML, Pewe L, Alvarez E, Rejas MT,
Perlman S, Enjuanes L (2008) Pathogenicity of
severe acute respiratory coronavirus deletion
mutants in hACE-2 transgenic mice. Virology
376:379–389
75. Pfefferle S, Krahling V, Ditt V, Grywna K,
Muhlberger E, Drosten C (2009) Reverse
genetic characterization of the natural genomic
deletion in SARS-coronavirus strain Frankfurt1 open reading frame 7b reveals an attenuating
function of the 7b protein in-vitro and in-vivo.
Virol J 6:131
76. DeDiego ML, Nieto-Torres JL, Jimenez-Guardeño JM, Regla-Nava JA, CastañoRodriguez C, Fernandez-Delgado R, Usera F,
Enjuanes L (2014) Coronavirus virulence
genes with main focus on SARS-CoV envelope
gene. Virus Res 194:124–137
77. Chung SH, Frese KK, Weiss RS, Prasad BV,
Javier RT (2007) A new crucial protein interaction element that targets the adenovirus
E4-ORF1 oncoprotein to membrane vesicles.
J Virol 81:4787–4797
78. Razanskas R, Sasnauskas K (2010) Interaction
of hepatitis B virus core protein with human
GIPC1. Arch Virol 155:247–250
79. Hsu EC, Lin YC, Hung CS, Huang CJ, Lee
MY, Yang SC, Ting LP (2007) Suppression of
hepatitis B viral gene expression by proteintyrosine phosphatase PTPN3. J Biomed Sci
14:731–744
80. Yu J, Li X, Wang Y, Li B, Li H, Li Y, Zhou W,
Zhang C, Wang Y, Rao Z, Bartlam M, Cao Y
(2011) PDlim2 selectively interacts with the
PDZ binding motif of highly pathogenic avian
H5N1 influenza A virus NS1. PLoS One 6:
e19511
81. Tomaic V, Gardiol D, Massimi P, Ozbun M,
Myers M, Banks L (2009) Human and primate
tumour viruses use PDZ binding as an evolutionarily conserved mechanism of targeting cell
polarity regulators. Oncogene 28:1–8
82. Suzuki T, Uchida-Toita M, Yoshida M (1999)
Tax protein of HTLV-1 inhibits CBP/p300mediated transcription by interfering with
recruitment of CBP/p300 onto DNA element
of E-box or p53 binding site. Oncogene
18:4137–4143
83. Wilson KC, Center DM, Cruikshank WW,
Zhang Y (2003) Binding of HTLV-1 tax oncoprotein to the precursor of interleukin-16, a T
cell PDZ domain-containing protein. Virology
306:60–67
84. Ohashi M, Sakurai M, Higuchi M, Mori N,
Fukushi M, Oie M, Coffey RJ, Yoshiura K,
Tanaka Y, Uchiyama M, Hatanaka M, Fujii M
(2004) Human T-cell leukemia virus type 1 tax
oncoprotein induces and interacts with a multiPDZ domain protein, MAGI-3. Virology
320:52–62
85. Song C, Wang W, Li M, Liu Y, Zheng D
(2009) Tax1 enhances cancer cell proliferation
via Ras-Raf-MEK-ERK signaling pathway.
IUBMB Life 61:685–692
86. Makokha GN, Takahashi M, Higuchi M,
Saito S, Tanaka Y, Fujii M (2013) Human
T-cell leukemia virus type 1 tax protein interacts with and mislocalizes the PDZ domain
protein MAGI-1. Cancer Sci 104:313–320
87. Blot V, Delamarre L, Perugi F, Pham D,
Benichou S, Benarous R, Hanada T, Chishti
AH, Dokhelar MC, Pique C (2004) Human
Dlg protein binds to the envelope glycoproteins of human T-cell leukemia virus type
1 and regulates envelope mediated cell-cell
fusion in T lymphocytes. J Cell Sci
117:3983–3993
88. Chen Q, Niu X, Xu Y, Wu J, Shi Y (2007)
Solution structure and backbone dynamics of
the AF-6 PDZ domain/Bcr peptide complex.
Protein Sci 16:1053–1062
89. Belotti E, Polanowska J, Daulat AM,
Audebert S, Thome V, Lissitzky JC, Lembo F,
Blibek K, Omi S, Lenfant N, Gangar A,
Montcouquiol M, Santoni MJ, Sebbagh M,
Aurrand-Lions M, Angers S, Kodjabachian L,
Reboul J, Borg JP (2013) The human
PDZome: a gateway to PSD95-disc largezonula occludens (PDZ)-mediated functions.
Mol Cell Proteomics 12:2587–2603
Chapter 14
Computational Design of PDZ-Peptide Binding
Nicolas Panel, Francesco Villa, Vaitea Opuu, David Mignon,
and Thomas Simonson
Abstract
This chapter describes two computational methods for PDZ–peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for
conformational sampling with a Poisson–Boltzmann (PB) Linear Interaction Energy for scoring. A new
CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide
variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative
binding free energies. A detailed protocol is described based on the Proteus CPD software. The mediumthroughput approach can be performed with standard MD and PB software, such as NAMD and Charmm.
For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around
0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of
parameter fitting before it can be applied, and its transferability to other protein families is still untested.
Key words Protein design, Ligand binding, MC simulation, Proteus program, Molecular mechanics,
Implicit solvent
1
Introduction
We focus here on the design of PDZ–peptide binding with computational approaches. One goal is to discover peptide ligands that
could inhibit or modulate the activity of a given PDZ protein. For
this, one should explore a space of peptide variants, perhaps allowing noncanonical amino acids (ncAAs) at selected positions, for
binding or stability. Another goal is to redesign the PDZ domain
itself, to alter its target binding and manipulate protein interaction
networks in vitro or in cells. For this, one would explore a space of
protein variants, where a few positions close to the peptide are
allowed to mutate. Both applications should potentially consider a
large space of sequences, so that medium or high-throughput
approaches are desirable. Both applications involve designing a
polypeptide, and so both are amenable to high-throughput, structure-based, computational protein design (CPD). CPD aims to
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021
237
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Nicolas Panel et al.
engineer proteins (and ligands) and optimize molecular properties
such as binding affinity and binding specificity [1–4]. It can explore
thousands of polypeptide sequences in 1–2 days using a desktop
computer. PDZ complexes can also be studied with mediumthroughput approaches that combine molecular dynamics
(MD) for conformational exploration with a simplified free energy
function for binding affinities. These approaches [5–15] can
explore a few dozen sequences in a few days using a few GPU
computers or a small Linux cluster.
Here, we outline both approaches and illustrate them by our
recent work on the Tiam1 PDZ domain. In a typical design project,
one might run a high-throughput calculation first, then characterize the top sequences with the medium-throughput approach. The
latter step might lead to additional high-throughput calculations,
perhaps involving additional mutating positions, and so on, in a
series of design cycles that would hopefully include experimental
steps. The illustrations below include high-throughput CPD protocols that can be run with our Proteus software [16, 17]. Our
medium-throughput approach uses a free energy function that
includes a Poisson–Boltzmann (PB) contribution, a surface area
(SA) contribution, and a van der Waals (vdW) contribution. It can
be run with standard MD and PB software.
1.1
General Issues
When engineering several amino acid positions, the mutation space
grows exponentially with the number of positions. If just three
positions are explored combinatorially, there are 8000 possible
sequences (more, if ncAAs are allowed). Each sequence variant
can occupy a vast number of conformations. Even if we use a
model where the protein backbone is held fixed, there are
thousands of rotamer combinations for the side chains at the
three mutating side positions, and millions if we consider the
whole binding interface. Another difficulty is that to engineer
PDZ–peptide binding, we should consider both the bound and
unbound states of each partner. If we engineer the protein, its
stability should be maintained, so that the unfolded state will also
play a role. If we care about specificity, we may have to design
against off-target binding, which would involve additional proteins
and states.
To address all these issues, major approximations are needed for
both conformational exploration and scoring. The first common
approximation is to use a molecular mechanics description of the
protein and peptide. Although some CPD tools like Rosetta use
knowledge-based energy functions [18, 19], many studies have
shown that molecular mechanics is a good approximation for
many design problems. The second common approximation is to
model solvent implicitly, as in continuum electrostatics. Formally,
this can be viewed as an averaging operation over solvent configurations [20]. This leads to the concept of a potential of mean force
Computational Design of Binding
239
[9, 21]. Implicit solvent models have been actively developed for
many years, both for medium and high-throughput applications.
They usually involve a continuum electrostatic component, such as
a Poisson–Boltzmann (PB) or Generalized Born (GB) energy term.
For high-throughput CPD, additional approximations are usually
necessary, outlined below.
1.2 PDZ–Peptide
Issues
PDZ–peptide binding presents specific difficulties. First, the
unbound peptide is quite flexible, and it is challenging to explore
its motions and quantify the effect of mutations on its flexibility and
entropy. Second, the binding interface is large and the affinity arises
from many small contributions, which should be accurately captured. Third, with such a large interface, many residues undergo
extensive burial upon binding, changing from a solvent-rich to a
solvent-poor environment. This change often leads to electronic
polarization, which is still a difficulty for molecular mechanics
models. Fourth, peptide phosphorylation regulates binding in
some cases, and phosphate–protein binding is also challenging to
model. Fifth, the use of ncAAs to enhance binding or peptide
stability means they must be part of the molecular mechanics
model. This often means a specific extension of the model is
needed.
1.3 Chapter
Overview
We first outline our high-throughput CPD approach. We describe
only briefly the technical details, which are available in published
articles [16, 22]. We describe a protocol that allows us to select
peptide variants based on their relative binding free energies, which
is of great interest. The approach is based on an adaptive Monte
Carlo method [22]. We present illustrative results for the Tiam1
PDZ domain. Next, we describe our medium-throughput
approach [23], which is readily applied to dozens of peptide or
protein variants (or a few hundred with more resources; roughly
one variant per day and per GPU card). The complex is simulated
with MD and explicit solvent. Then the binding free energy is
computed with a free energy function that combines a Poisson–
Boltzmann description of solutes and solvent, along with additional
nonpolar SA and vdW free energy terms. We include selected results
for the Tiam1 PDZ domain binding to a collection of peptides
[23], some of which were taken from CPD predictions. Figure 1
shows Tiam1 bound to the Syndecan-1 peptide (Sdc1), which
corresponds to the C-terminus of its natural target protein.
2
High-Throughput Design of PDZ–Peptide Binding
Many successful CPD examples have been reported in recent years
[3, 24–30]. Many were obtained with energy functions that
included knowledge-based terms, such as those in the Rosetta
energy function. Proteus, on the other hand, relies on “physics-
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Nicolas Panel et al.
Fig. 1 The complex between the Tiam1 PDZ domain and the Sdc1 peptide (cross-eyed stereo). The peptide is
yellow; its residues are labeled with their type. The Sdc1 sequence is 7TKQEEFYA0
based” CPD. One of its characteristics is the energy function drawn
from molecular mechanics. The other is its sampling approach,
which uses adaptive Monte Carlo to target the binding free energy,
as opposed to simpler properties like the bound-state energy. The
CPD methods described below are available (and further documented) in the recently released Proteus 3.0 package (https://proteus.
polytechnique.fr) [17].
2.1 Model
Ingredients
and System Setup
We use a molecular mechanics energy function along with an
implicit solvent model that contains a Generalized Born
(GB) term and a nonpolar term:
2.1.1 Energy Model
E ¼ E bond þ E angle þ E dihedral þ E improper þ E vdW þ E Coulomb
þ E GB þ E NP
ð1Þ
The first six terms describe the internal and nonbonded contributions to the potential energy of the protein or peptide, and are
borrowed from the Amber ff99SB molecular mechanics energy
function [31]. The next two terms capture solvent effects via a
GB approximation for electrostatic effects and a nonpolar term.
This can be either an accessible surface area (SA) term or a Lazaridis–Karplus (LK) term [32].
2.1.2 Structures
We start from an X-ray complex between the Tiam1 PDZ domain
(called “Tiam1”) and the Syndecan-1 octapeptide, Sdc1
[33]. Hydrogen positions are added and a slight energy minimization is done (200 steps) to remove poor steric contacts. To model
the unbound peptide, the protein atoms are removed. We assume a
few amino acid positions on the peptide are to be redesigned. We
refer to these as “active” positions. All other peptide and protein
positions are “inactive”: they will explore rotamers but not mutate.
Computational Design of Binding
241
At all inactive positions, rotamers from a library are positioned. We
use the Tuffery rotamer library, slightly extended [34, 35]. It has
about ten rotamers per amino acid type. At the active positions, we
position all the rotamers for all the allowed amino acid types,
around 200 if all types are allowed. In fact, we do not mutate into
Gly or Pro, so there are 18 possible types. With four active positions, there are 104,976 possible sequences. At this point, each
residue in the system has around 10 rotamer conformations in
place, or 200 if it is active. All these are recapitulated in a single
PDB file. The protein and peptide backbone conformations will be
held fixed. Backbone flexibility will be modeled implicitly, through
the protein dielectric constant. One can also perform several design
calculations, each with a slightly different backbone conformation,
or a single, multibackbone calculation. This last protocol is significantly more complicated and expensive [36], and is not
described here.
2.1.3 Unfolded
Protein State
If we allow mutations (active positions) in the protein, we will need
to evaluate their effect on stability and eliminate highly destabilizing mutations. We model the unfolded state through an energy
function that is a sum over the amino acid positions [16, 37]:
X uf
E uf ¼
E I ðt I Þ
ð2Þ
I
uf
The sum is over all amino acids I. Each term E I ðt I Þ depends on
the residue type tI. There are no contributions from interresidue
interactions, meaning that in the unfolded state, each residue interacts with solvent and nearby backbone groups but not the surrounding side chains. This seems reasonable for a fully extended
uf
polypeptide chain. In practice, the individual contributions E I ðt I Þ
are computed from the folded structure as follows: we compute the
energy of each side chain, including its interactions with solvent, its
own backbone, and the backbone of the previous and following
residues. This rather crude model is used below to filter out highly
destabilizing mutations. Note that for some other applications,
uf
especially whole protein redesign, the E I ðt I Þ must be chosen
more carefully, and normally require a complex empirical
optimization [37].
2.1.4 Energy Matrix
We precompute and store in a matrix the interaction energies
between all residue pairs, taking into account all residue types
(active positions) and rotamers (active or inactive). This calculation
is done by the protX program, through a library of command
scripts, using the energy function of Eq. (1). Given a particular
set of mutations and rotamers, the energy can then be obtained by
summing contributions from this matrix, with one important
caveat. The solvation terms (GB, especially) are not rigorously
242
Nicolas Panel et al.
pairwise-additive: they are not a sum over residue pairs. Specifically,
the GB model assigns to each atom i a “solvation radius” bi, which
depends on the conformation of the whole system [9]. To overcome this, two methods are available. We can adopt a “Native
Environment Approximation” (NEA), where each solvation radius
is computed for the wildtype or native structure, then kept fixed
[38]. Or, we can adopt a modified GB model, which computes
more information and stores it in the energy matrix, then requires
additional operations during the subsequent MC simulations. This
variant is called the Fluctuating Dielectric Boundary method [39].
2.1.5 Monte Carlo
Simulations to Explore
Sequences and Structures
The energy matrix is read by the C module protMC, which explores
the space of sequences and structures. Three exploration methods
are available; only Monte Carlo (MC) is considered here
[40, 41]. MC can use a single “replica”, exploring a single trajectory. Or it can use multiple replicas, usually 4–8, with distinct
temperatures, which occasionally exchange their temperatures.
The method is known as “Replica Exchange” MC, or REMC. All
the methods output multiple “snapshots”, sampled along the MC
trajectory. MC moves correspond to rotamer changes at one or two
positions, mutations at one or two positions, or a rotamer change at
one position and a mutation at another. The REMC temperatures
usually range from about 50 to 1000 K, with the very hot replica
easily moving among local energy minima for effective sampling.
Adaptive MC is also possible, as outlined in the next section.
2.1.6 Proteus Software
Files and Documentation
Proteus 3.0 is freely available to academic and government scientists, from https://proteus.polytechnique.fr. Industrial scientists
should contact the corresponding author. The distribution includes
source code, binaries for Intel processors, extensive test cases, and
detailed documentation.
2.2 Adaptive
Landscape Flattening
to Design PDZ–Peptide
Binding Affinity
To design ligand binding means optimizing a free energy difference
between bound and unbound states. This is not tractable by most
CPD methods, such as simulated annealing or plain Monte Carlo
(MC). Most studies have used heuristic methods that optimize the
bound state energy, a very different property. Recently, a new
method was proposed, using MC simulations and adaptive importance sampling. The energy landscape in sequence space is flattened
adaptively for the unbound state, over the course of an MC simulation, thanks to a bias potential [22, 42]. The bias B is constructed
such that all sequences reach comparable populations. B is then
essentially the sequence free energy with its sign changed. Next, the
bias is included in a simulation of the complex, where it “subtracts
out” the unbound state. Thus, negative design of the unbound
state is achieved. Remarkably, the result is a Boltzmann distribution
where sequence populations measure their affinities.
2.2.1 General Method
Computational Design of Binding
2.2.2 Stage 1: Flattening
the Unbound State
243
MC will be run for the unbound state with the protMC program,
controlled by a configuration file adapt.conf. Since we plan to
mutate the peptide, the unbound simulation will correspond to
the peptide alone. If we wanted to mutate the protein, we would
simulate the protein alone. The file adapt.conf indicates which
mutations are allowed for positions that are active (four peptide
positions in our application [22]). During the adaptation, it is best
uf
to include reasonable values for the unfolded energies E I ðt I Þ
(Eq. 2), which are readily obtained with Proteus, based on the
extended peptide picture (above). Thus, adapt.conf includes lines
such as the following:
<Ref_Ener>
ALA 7.54
ARG -52.58
Etc
</Ref_Ener>
Adapt.conf also contains information that controls the form of the
bias potential and its update schedule. Full details are in the Proteus
manual (https://proteus.polytechnique.fr). At this point, we run
protMC.
protMC.exe < adapt.conf > adapt.log
Output files are:
l
bias.dat: evolution of the bias during the MC trajectory,
l
proteus_adapt.seq: visited sequences,
l
output.ener: the energy of visited sequences.
At this point, we copy the final bias from bias.dat to a new file, bias.
in.
2.2.3 Stage 2: Simulating
the Bound State
The next step is to run an MC simulation of the complex, including
the bias potential (which effectively subtracts out the unbound
state). Thus, simulating the complex with the apo bias will now
lead to peptide sequences that are populated according to their
Tiam1 binding free energy (sic). The MC simulation is controlled
by a file similar to adapt.conf above. No adaptation is done; rather
the obtained bias is made available in the file bias.in. Once the
simulation is done, affinity-based sampling is finished.
Sequence populations will now lead directly to binding affinities. For two sequences s and r sampled in both states, we denote
p’s, p’r the biased holo populations and ps, pr the biased apo populations (with the same bias). We can obtain the binding free energy
difference as
244
Nicolas Panel et al.
p0
p
ΔG s ΔG r ¼ kT ln s0 þ kT ln s
pr
pr
ð3Þ
Extraction of the populations from the MC simulations and calculation of the free energy is done by Proteus with a python script. For
sequences of interest, such as the tightest binders, 3D structure
models can be computed from the rotamer information with a bash
script.
2.2.4 Application
to the Tiam1–Sdc1
Complex
3
An application to the Tiam1–Sdc1 complex was reported recently
[22]. Four of the last five peptide positions were allowed to mutate
into all types except Gly or Pro. The position numbers were 4 to
1, following the usual “backward” convention for PDZ binders.
The C-terminal position (position 0) was kept as in the wild-type
peptide (Ala), because the Sdc1 backbone arrangement does not
allow large side chains at this position. There were 104,976 possible
sequences. Thanks to the adaptive method, a large fraction were
sampled. For nine variants, relative binding free energies were
available from experiment or high-level, alchemical MD free energy
simulations. Excluding one large error, the mean unsigned errors
from eight variants was 0.8 kcal/mol. Figure 2 shows the sequences
sampled, in the form of a sequence logo, with populations given by
their relative binding free energies. The logo is compared to one
that represents an experimental library of Tiam1-binding peptides
[43]. Positions P0 and P1 are the most important for PDZ binding
specificity. Position P0 occupies three main types experimentally, C,
A, and F, but was held fixed during the simulations. Of the four
positions allowed to mutate, P1, P3, and P4 are highly variable
in both the MC and the experimental logos. Of the top ten MC
types at these positions, 7 or 8 are present in the experimental logo,
and vice versa, with somewhat different occupancies. Position P2
is more conserved, both experimentally and in the simulations. Of
the top four experimental types, Y, F, M, T, all but T are in the top
five MC types. While T has less than 1% occupancy in the MC
sequences, the chemically similar types A, C, and S are highly
populated. Overall, the two logos are in reasonable agreement.
A Medium-Throughput Design Approach
After high-throughput design, one can use a more costly model to
characterize a few dozen of the top CPD candidates with increased
accuracy [44]. The model uses MD with explicit solvent to sample
conformations, then scores them with a free energy function where
solvent is modeled implicitly. MD is done for the PDZ–peptide
complex, typically for 80–100 ns. Several hundred snapshots are
taken from the trajectory. The unbound state is modeled by using
the same snapshots and simply separating the two partners. We refer
to this as a single-trajectory approach. A two-trajectory variant that
245
Adaptive MC
Experimental
Computational Design of Binding
Fig. 2 Top: Sequence logo from an experimental library of peptides that bind
Tiam1 [43]. Each column corresponds to a peptide position; P0 is the
C-terminus; the last five positions are shown. Types have heights proportional
to their abundancy. Bottom: Logo from the MC simulation of the Tiam1–peptide
complex, where sequences are populated by affinity
uses separate simulations of the unbound peptide is also described.
The free energy function uses implicit solvent, which includes a
Poisson–Boltzmann electrostatic term plus a Surface Area (SA) and
a van der Waals (vdW) term. Taking the free energy difference
between the bound and unbound structures and averaging over
all the snapshots we obtain a binding free energy. Obviously, some
free energy components are not accounted for, such as translational
entropy. But they are expected to cancel out when we compare
several peptide (or protein) variants. The free energy function
requires some parameterization and testing before it can be applied.
Here, we outline the method, its parameterization using experimental binding free energies and its application to designed Sdc1
sequences. The method is also schematized in Fig. 3.
3.1 Explicit-Solvent
MD to Characterize
PDZ–Peptide
Complexes
MD is done with explicit solvent and only a few restraints on the
complex. Thus, the fixed backbone approximation from the CPD
stage is removed. Simulations lengths of 50–100 ns are used, which
allows the free energy function to converge. It also gives time for
the backbone to rearrange, if necessary. If, for a predicted variant,
the backbone rearranges significantly and/or the peptide starts to
detach, it suggests the particular complex is unstable and may
invalidate the CPD prediction.
246
Nicolas Panel et al.
Fig. 3 Medium-throughput protocol with MD sampling and PB/LIE scoring. The complex is simulated with
explicit solvent; snapshots are extracted; solvent positions are discarded. The unbound state is obtained from
each snapshot by removing one partner or the other. The snapshots are processed by a continuum model to
obtain the electrostatic free energy component; surface areas and the van der Waals interaction are computed
to obtain the nonpolar components. The binding free energy ΔG is a weighted sum of the components, timeaveraged (brackets) [44]
3.1.1 Conformational
Restriction of the Peptide
N-Terminus
The peptide positions most important for binding are near its
C-terminus. A difficulty is that with a short, 8-residue peptide,
the N-terminus has large fluctuations, detaching and reattaching
on a 10 ns timescale. These fluctuations cannot be adequately
sampled with 100 ns MD runs. We expect they are not too dependent on mutations near the C-terminus and so it is better to
suppress them. Thus, we introduced weak, “flat-bottomed”
restraints at the peptide N-terminus, which only acted if the first
two residues moved more than 1 Å away from their starting position, as measured by their contact distances to the PDZ domain.
3.1.2 Force Field and MD
Simulations
We used the Amber force field, consistent with the highthroughput CPD stage. For PDZ domains, it is important to use
the recent ff14SB release, which gives improved helix backbone
structures. We used the TIP3P water model, which has been extensively tested in combination with the Amber force field. The combination gives excellent agreement with available X-ray structures
Computational Design of Binding
247
for the overall structure during microsecond MD runs, and with
experimental backbone amide order parameters, which measure
conformational fluctuations.
We modeled PDZ–peptide complexes based on X-ray structures involving four peptides: Sdc1, Caspr4, Neurexin, and a “consensus” peptide from a combinatorial peptide library [45]. The
peptides were bound to either the wild-type Tiam1 PDZ domain
(WT) or a variant containing four amino acid changes (quadruple
mutant or QM). The four complexes were WT–Sdc1 (PDB 4GVD)
[33], WT–consensus (PDB 3KZE) [46], QM–Caspr4 (PDB
4NXQ) [47], and QM–Neurexin (PDB 4NXR) [47].
Our truncated icosahedral simulation box included around
10,900 water molecules. Each PDZ–peptide variant was prepared
by immersing it in the same solvent box, deleting overlapping
waters, and running about 500 ps of equilibration at room temperature with gradually decreasing harmonic restraints, initially on all
solute atoms and finally on only backbone Cα’s. Structure preparation was done with the protX module of Proteus, while equilibration was done with NAMD. Other programs can also be used, such
as Charmm, Amber, or Gromacs. MD production was run for
40 ns, then continued until the free energy function converged or
100 ns were reached. This took up to 2 days on a single GPU
processor.
3.2 Relative Binding
Free Energies
3.2.1 The Free Energy
Function
To obtain the binding free energy estimate ΔG, we used the following ansatz for the free energy:
ΔG ¼ αhΔE vdW i þ βhΔG elec i þ γ hΔA i þ δ
ð4Þ
Here, α, β, and γ are adjustable constants. ΔGelec is an electrostatic
free energy difference between the bound and unbound states,
computed with a PB model. Brackets indicate averaging over the
structural snapshots taken at regular intervals along the MD trajectory of the solvated complex. To represent the unbound state, we
took each snapshot from the MD trajectory of the complex and
simply moved the protein and the peptide apart. Thus, the same
snapshot is used for all three solutes. ΔA is the change in the solute
molecular surface upon binding (which is negative). ΔEvdW is the
van der Waals interaction energy between the protein and the
peptide. Solute–solvent and solvent–solvent van der Waals contributions are not explicitly included. PB calculations were done with
the Charmm program, while the SA and vdW terms were computed
with protX. The solute dielectric constant ϵ S was set to 8. The last
term, δ, is a constant that vanishes when we consider the relative
binding free energies ΔΔG of the various complexes. We refer to
the free energy ansatz as a PB/LIE free energy, for PB Linear
Interaction Energy, since Eq. (4) is a weighted sum of interactions.
248
Nicolas Panel et al.
3.2.2 Fitting
to Experimental Binding
Free Energies
Experimental binding free energies were available for 44 complexes
[44]. Four very weak binders were excluded. To obtain relative
values, the free energies were compared to either the WT:Sdc1
value or the QM:Caspr4 value, depending on which X-ray structure
was used to model each variant. This left 38 independent ΔG
values. Three involving the neurexin peptide were left aside and
the remaining 35 were used to fit the adjustable constants α, β, and
γ. Extensive cross-validation was done, where subsets of the data
were excluded from the fit; this gave similar fitted values and errors.
3.3
For the 35 complexes with experimental ΔG values, the mean
unsigned and rms errors were 0.43 and 0.55 kcal/mol. The Pearson correlation between the experimental and computed values was
R ¼ 0.64. The three largest errors were 1.31, 1.13, and 1.09 kcal/
mol and included two Caspr4 complexes. Thus, there were no very
large errors and the mean error is very small, and corresponds to
chemical accuracy (less than the thermal energy kT).
Selected Results
3.3.1 Mean Errors
3.3.2 Scoring Sequences
from CPD
From the top 30 peptide variants predicted by CPD to be the
tightest binders, we chose 14 for medium-throughput study.
Results (reported here) are given in Table 1. The adaptive MC
results are also given. MC predicted that several variants should
bind more strongly than Sdc1. PB/LIE, on the other hand, predicts that 12 of the variants bind less strongly, with ΔGs that are
between 0.1 and 0.6 kcal/mol less favorable than Sdc1. Two are
predicted to bind as strongly as Sdc1. None show improved binding. Since the mean PB/LIE error is around 0.5 kcal/mol, we
conclude that at best, the high-throughput design may have produced a few peptides with weakly improved affinities. It may be
possible to improve binding by designing other peptide positions
or allowing ncAAs.
3.4 Other Variants
of the Model
Claims are sometimes made that PB is a better electrostatic model
than GB for binding and other properties. Here, switching to GB
gave very slight increases of mue and rmse, to 0.55 and 0.66 kcal/
mol, respectively. Notice that we used GB for CPD.
3.4.1 GB Instead of PB
3.4.2 Lazaridis–Karplus
Instead of SA
The Lazaridis–Karplus solvent model was parameterized elsewhere
for nonpolar solvation effects [32]. It was applied to the PDZ–
peptide complexes without any reparameterization, in combination
with the GB electrostatic term. The mue and rmse values were 0.59
and 0.69 kcal/mol, respectively, almost the same as with GB + SA.
3.4.3 Two-Trajectory
Model for Peptide Flexibility
The flexibility of the unbound peptide is in principle sequencedependent, which could influence relative affinities. Therefore, we
also considered a model where the binding process is divided into
two steps. In the first, we apply restraints to the peptide, forcing it
to be close to its bound conformation; in the second it binds
Computational Design of Binding
249
Table 1
Relative Tiam1 binding free energies ΔΔG of designed peptides from PB/LIE
ΔΔG
Sequencea
PBb
vdWb
SAb
c
ΔG
c
PB/LIE
d
ETMNA
3.25
48.10
1310.1
3.46
0.05
1.2
EEMNA
2.99
49.48
1307.6
3.49
0.02
1.1
EEFYA
2.84
58.67
1349.3
3.51
0.00
0.0
YECEA
3.01
55.62
1371.0
3.61
0.11
1.3
YTCDA
2.26
50.67
1313.2
3.77
0.16
1.3
ESMTA
2.24
48.65
1305.9
3.70
0.18
1.2
YSCDA
1.88
49.70
1295.0
3.71
0.20
1.3
ETMTA
2.50
49.47
1331.0
3.71
0.20
1.1
ETMEA
1.06
46.81
1247.8
3.79
0.28
1.1
EEMSA
1.64
49.27
1329.5
3.92
0.41
1.2
YNCTA
+1.20
38.10
1111.0
3.98
0.47
1.3
YTCVA
1.87
57.31
1398.6
3.98
0.47
1.3
YTCTA
0.93
50.07
1321.8
4.05
0.54
1.5
YNCYA
1.56
57.28
1401.7
4.07
0.56
1.3
YSCTA
0.46
49.27
1311.0
4.14
0.63
1.4
Proteus
PB/LIE binding free energies ΔG (Eq. 4) for selected peptide sequences, in kcal/mol. ΔΔG values are relative to Sdc1
(in bold, “EEFYA”)
a
The five C-terminal amino acids are listed. The N-terminal ones are always TKQ
b
Specific ΔG contributions, in kcal/mol or Å2 (SA)
c
Component weights (Eq. 4) were α ¼ 0.02 (vdw), β ¼ 0.25 (elec), and γ ¼ 4 cal/mol/Å2
d
CPD values (adaptive MC with Proteus)
Tiam1. The first free energy contribution is deduced from the
fraction of bound conformations in MD simulations of the
unbound peptide. The second is computed from the PB/LIE
model as before. Hence, two trajectories are used (per peptide
variant). In practice, the first contribution is small and noisy, and
several 100 ns are needed for convergence. Its sequencedependence is weak. With this model, the rms error did not
improve.
3.4.4 Three-Trajectory
Model
For two complexes, WT:Sdc1 and QM:Caspr4, we performed
much longer MD simulations and applied a 3-trajectory method
to the PB free energy component. The unbound peptides were
simulated for 400 ns, the two complexes for 500 ns, and the
unbound proteins for 1000 ns each. The binding free energy
difference between the two variants was in excellent agreement
with the single-trajectory value.
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Nicolas Panel et al.
3.4.5 Comparison
to some PBSA or GBSA
Approaches Applied
to Other Systems
4
Table 2 summarizes the performance of some earlier PBSA and
GBSA approaches. Several achieve high correlations with experiments. Only five outperform the Null model and only one achieves
(for a much smaller dataset) the small errors reported here.
Concluding Notes
We conclude with a series of practical observations, or “lessons
learned” that we have derived from our work and that should
help readers apply the methods presented above to related systems:
1. High-throughput CPD of the PDZ protein for binding can be
handled in the same way as the peptide design illustrated in
Subheading 2. A slightly simpler approach was reported earlier
[37] that did not employ (yet-undiscovered) adaptive MC.
2. We recently reported redesign of the entire PDZ protein, the
first successful whole-protein design with a nonempirical,
physics-based energy function [60].
3. A key to the accurate PB/LIE predictions described in Subheading 3 was the use of the Eq. (4) empirical ansatz. A pure
PBSA or GBSA free energy is unlikely to succeed without
empirical weights.
4. The solute dielectric constant ϵ S for the PB component (Eq. 4
in Subheading 3) is an empirical parameter. Choosing a value of
8 is physically reasonable [61]. Choosing a different value (such
as 4) led to a nearly proportional change in the PB weight β
(Eq. 4) and thus similar results.
5. Another key to PB/LIE success was good conformational
sampling, thanks to moderately long MD runs and suppression
of slow (evidently unimportant) N-terminal fluctuations of the
peptide.
6. Our PB/LIE model (Subheading 3) made no attempt to
model contributions from conformational entropy changes;
we assume normal mode or quasi-harmonic models would
not be predictive for our systems and MD trajectory lengths.
7. The PB/LIE model could not handle very weak binders, which
were left out of the fit. Despite using MD, it also could not
handle the conformational changes between Sdc1-like peptides
and Cask-like peptides, which have distinctly different backbone arrangements. Therefore, these two data sets each had
their own reference complex [23].
8. The tradeoff for the excellent PB/LIE accuracy (Subheading
3.3) is the need to fit α, β, γ. Transferability to other PDZ
domains should be good but was not yet tested.
Target
DHFR
DHFR
Alpha-thrombin 7
Avidin
Cytochrome C
Neuraminidase
P450cam
Penicillopepsin
HIV-I protease
FXa
Hsp90
[50]
[50]
[51]
[51]
[51]
[51]
[51]
[51]
[52]
[52]
[52]
RNA aptamer
RNA aptamer
[55]
MMP-2
[54]
Ligands: RNA
[53]
Ligands: peptidomimetics
HIV-I protease
[49]
6
5
8
16
20
20
7
9
8
18
7
22
22
12
Avidin
9
PB
PB
PB
PB
PB
PB
PB
PB
PB
PB
PB
PB
GB
PB
PB
PB
?
1
1
1
1
1
2
1
4
1
1
4
1
1
?
1
Sample size Elec. model ϵ S
[48]
Ligands: small molecules
Reference
Table 2
Performance of PBSA and GBSA in selected studies
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
SA
SA
PB, vdW, SA
NM
–
NM
PB, vdW, SA, TS NM
PB, vdW, SA, TS NM
PB, vdW, SA, TS NM
SA
SA
SA
SA
SA
SA
SA
SA
SA
SA
3.0
1.2
0.8
1.4
[12.8; 6.8] 2.8
[16.5; 9.9] 1.0
[12.8; 8.6] 0.6
[10.5; 5.1] 1.2
3.8
6.6
3.5
[11.5; 6.0] 6.5
[1.1; 5.6]
3.6
6.1
5.8
[7.9; 5.5]
[15.0; 7.8] 3.0
2.9
1.7
1.5
2.2
1.8
1.5
2.0
2.3
0.9
2.6
0.8
(continued)
0.75 1.2
0.66 1.2
0.84 1.9
0.63 1.6
0.54 0.8
0.72 1.8
0.41 2.1
0.68 0.8
0.68 2.2
11.9 12.4 0.30 0.7
[11.5; 3.7] 2.3
[7.1; 3.8]
5.3
[20.4; 4.5] 7.8
0.93 4.7
2.6
[12.4; 4.0] 19.8 20.7 0.80 2.1
8.1
2.7
1.3
[15.0; 5.5] 16.4 18.0 0.94 2.4
8.0
[15.0; 5.5] 7.2
0.74 1.1
4.9
2.7
1.2
[11.9; 7.8] 1.1
0.96 4.1
mue rmse
__Nulld__
0.91 2.4
3.8
mue rmse R
Performance
[5.0;20.4] 3.3
ΔG
a
Weighted terms bEntropy method rangec
Computational Design of Binding
251
Target
HIV-I protease
HIV-1 gp41
AIRE-PHD1
[57]
[58]
[59]
35
9
29
4
14
PB
PB
PB
PB
PB
SA
SA
8
1
PB, vdW, SA
SA
var SA
1
1
Sample size Elec. model ϵ S
–
NM
QH
NM
NM
2.0
[0.6; 1.3]
[0.2; 2.1]
[2.4; 1.1]
0.4
6.7
–
0.6
7.7
–
0.6
2.3
0.64 0.4
0.74 0.5
0.71 –
0.96 1.0
0.5
0.6
–
1.2
0.9
mue rmse
__Nulld__
0.82 0.8
mue rmse R
Performance
[13.8;-10.6] 0.5
[0.8; 2.6]
ΔG
a
Weighted terms bEntropy method rangec
Free energies in kcal/mol
a
Terms in the free energy ansatz
b
Method used to estimate conformational entropy contributions: normal mode (NM) or quasi-harmonic (QH) approximation
c
Range of experimental binding free energies
d
Null model (all binding free energies equal to the experimental mean)
Our work [23] Tiam1
Abl-SH3
[56]
Ligands: peptides
Reference
Table 2
(continued)
252
Nicolas Panel et al.
Computational Design of Binding
253
9. A third, low-throughput approach (not described above) introduces mutations alchemically, one or two at a time, during
explicit solvent MD. It does not require any parameter adjustment. It was applied to Tiam1–peptide binding and gave even
higher accuracy [44].
References
1. Malisi C, Schumann M, Toussaint NC et al
(2012) Binding pocket optimization by
computational protein design. PLoS One 7:
e52505–e52505
2. Feldmeier K, Höcker B (2013) Computational
protein design of ligand binding and catalysis.
Curr Opin Chem Biol 17:929–933
3. Tinberg CE, Khare SD, Dou J et al (2013)
Computational design of ligand-binding proteins with high affinity and selectivity. Nature
501:212–216
4. Stoddard BL (ed) (2016) Methods in molecular biology: design and creation of ligand binding proteins. Springer Verlag, New York
5. Srinivasan J, Cheatham TE, Cieplak P et al
(1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidateDNA helices. J Am Chem Soc 120
(37):9401–9409
6. Jorgensen WL (2004) The many roles of computation
in
drug
discovery.
Science
303:1813–1818
7. Brandsdal BO, Österberg F, Almlöf M et al
(2003) Free energy calculations and ligand
binding. Adv Protein Chem 66:123–158
8. Carlsson J, Andér M, Nervall M et al (2006)
Continuum solvation models in the linear
interaction energy method. J Phys Chem B
110:12034–12041
9. Simonson T (2007) Free energy calculations:
approximate methods for biological macromolecules. Springer Ser Chem Phys 86:423–461
10. Gallicchio E, Levy RM (2011) Recent theoretical and computational advances for modeling
protein-ligand binding affinities. Adv Protein
Chem Struct Biol 85:27–80
11. Simonson T (2013) Protein: ligand recognition: simple models for electrostatic effects.
Curr Pharm Des 19:4241–4256
12. Harris RC, Mackoy T, Fenley MO (2015) Problems of robustness in Poisson-Boltzmann
binding free energies. J Chem Theory Comput
11:705–712
13. Wang C, Nguyen PH, Pham K et al (2016)
Calculating protein-ligand binding affinities
with MMPBSA: method and error analysis. J
Comput Chem 37:2436–2446
14. Chakavorty A, Li L, Alexov E (2016) Electrostatic component of binding energy: interpreting predictions from Poisson–Boltzmann
equation and modeling protocols. J Comput
Chem 37:2495–2507
15. Katkova EV, Onufriev AV, Aguilar B et al
(2017) Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand
binding. J Mol Graph Model 72:70–80
16. Simonson T, Gaillard T, Mignon D et al (2013)
Computational protein design: the proteus
software and selected applications. J Comput
Chem 34:2472–2484
17. Simonson T (2019) The Proteus software for
computational protein design, Ecole Polytechnique, Paris. https://proteus.polytechnique.fr
18. Li Z, Yang Y, Zhan J et al (2013) Energy functions in de novo protein design: current challenges and future prospects. Annu Rev Biophys
42:315–335
19. Alford RF, Leaver-Fay A, Jeliazkov JR et al
(2017) The Rosetta all-atom energy function
for macromolecular modeling and design. J
Chem Theory Comput 13:3031–3048
20. Simonson T (2015) The physical basis of ligand
binding. In: Cavasotto CN (ed) In silico drug
discovery and design: theory, methods, challenges, and applications. CRC Press
21. Roux B, Simonson T (1999) Implicit solvent
models. Biophys Chem 78:1–20
22. Villa F, Panel N, Chen X et al (2018) Adaptive
landscape flattening in amino acid sequence
space for the computational design of protein:
peptide
binding.
J
Chem
Phys
149:072302–072302
23. Panel N, Sun YJ, Fuentes EJ et al (2017) A
simple PB/LIE free energy function accurately
predicts the peptide binding specificity of the
Tiam1 PDZ domain. Front Mol Biosci
4:65–65
24. Saven JG (2010) Computational protein
design: advances in the design and redesign of
biomolecular nanostructures. Curr Opin Colloid Interface Sci 15:13–17
254
Nicolas Panel et al.
25. Pantazes RJ, Grisewood MJ, Maranas CD
(2011) Recent advances in computational protein design. Curr Opin Struct Biol 21:467–472
26. Der BS, Kuhlman B (2013) Strategies to control the binding mode of de novo designed
protein interactions. Curr Opin Struct Biol
23:639–646
27. Moal IH, Moretti R, Baker D et al (2013)
Scoring functions for protein-protein interactions. Curr Opin Struct Biol 23:862–867
28. Zanghellini A (2014) De novo computational
enzyme design. Curr Opin Biotechnol
29:132–138
29. Khoury GA, Smadbeck J, Kieslich CA et al
(2014) Protein folding and de novo protein
design for biotechnological applications.
Trends Biotechnol 32:99–109
30. Simonson T, Ye-Lehmann S, Palmai Z et al
(2016) Redesigning the stereospecificity of
tyrosyl-tRNA synthetase. Proteins 84:240–253
31. Cornell WD, Cieplak P, Bayly CI et al (1996) A
second generation force field for the simulation
of proteins, nucleic acids, and organic molecules
J.
Am.
Chem.
Soc.
1995,
117, 51795197. J Am Chem Soc
118:2309–2309
32. Michael E, Polydorides S, Simonson T et al
(2017) Simple models for nonpolar solvation:
parameterization and testing. J Comput Chem
38:2509–2519
33. Liu X, Shepherd TR, Murray AM et al (2013)
The structure of the Tiam1 PDZ domain/
phospho-syndecan1 complex reveals a ligand
conformation that modulates protein dynamics. Structure 21:342–354
34. Tuffery P, Etchebest C, Hazout S et al (1991)
A new approach to the rapid determination of
protein side chain conformations. J Biomol
Struct Dyn 8:1267–1289
35. Gaillard T, Panel N, Simonson T (2016) Protein side chain conformation predictions with
an MMGBSA energy function. Proteins
84:803–819
36. Druart K, Bigot J, Audit E et al (2016) A
hybrid Monte Carlo scheme for multibackbone
protein design. J Chem Theory Comput
12:6035–6048
37. Mignon D, Panel N, Chen X et al (2017)
Computational design of the Tiam1 PDZ
domain and its ligand binding. J Chem Theory
Comput 13:2271–2289
38. Polydorides S, Simonson T (2013) Monte
carlo simulations of proteins at constant pH
with generalized born solvent, flexible sidechains, and an effective dielectric boundary. J
Comput Chem 34:2742–2756
39. Villa F, Mignon D, Polydorides S et al (2017)
Comparing pairwise-additive and many-body
generalized born models for acid/base calculations and protein design. J Comput Chem
38:2396–2410
40. Frenkel D, Smit B (1996) Understanding
molecular simulation, Computational science
series, vol 1. Academic Press, New York
41. Mignon D, Simonson T (2016) Comparing
three stochastic search algorithms for computational protein design: Monte Carlo, replica
exchange Monte Carlo, and a multistart,
steepest-descent heuristic. J Comput Chem
37:1781–1793
42. Bhattacherjee A, Wallin S (2013) Exploring
protein-peptide binding specificity through
computational peptide screening. PLoS Comput Biol 9:e1003277
43. Shepherd TR, Hard RL, Murray AM et al
(2011) Distinct ligand specificity of the Tiam1
and Tiam2 PDZ domains. Biochemistry
50:1296–1308
44. Panel N, Villa F, Fuentes EJ et al (2018) Accurate PDZ/peptide binding specificity with
additive and polarizable free energy simulations. Biophys J 114:1091–1102
45. Songyang Z, Fanning AS, Fu C et al (1997)
Recognition of unique carboxyl-terminal
motifs by distinct PDZ domains. Science
275:73–77
46. Shepherd TR, Klaus SM, Liu X et al (2010)
The Tiam1 PDZ domain couples to syndecan1
and promotes cell-matrix adhesion. J Mol Biol
398:730–746
47. Liu X, Speckhard DC, Shepherd TR et al
(2016) Distinct roles for conformational
dynamics in protein-ligand interactions. Structure 24:2053–2066
48. Kuhn B, Kollman PA (2000) A ligand that is
predicted to bind better to avidin than biotin:
insights from computational fluorine scanning.
J Am Chem Soc 122:3909–3916
49. Wang L, Magliery TJ, Liu DR et al (2000) A
new functional suppressor tRNA/aminoacyltRNA synthetase pair for the in vivo incorporation of unnatural amino acids into proteins
[16]. J Am Chem Soc 122:5010–5011
50. Rastelli G, Del Rio A, Degliesposti G et al
(2010) Fast and accurate predictions of binding free energies using MM-PBSA and
MM-GBSA. J Comput Chem 31:797–810
51. Hou T, Wang J, Li Y et al (2011) Assessing the
performance of the MM/PBSA and
MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular
dynamics simulations. J Chem Inf Model
51:69–82
Computational Design of Binding
52. Ben-Shalom IY, Pfeiffer-Marek S, Baringhaus
KH et al (2017) Efficient approximation of
ligand rotational and translational entropy
changes upon binding for use in MM-PBSA
calculations. J Chem Inf Model 57:170–189
53. Hou T, Guo S, Xu X (2002) Predictions of
binding of a diverse set of ligands to
gelatinase-A by a combination of molecular
dynamics and continuum solvent models. J
Phys Chem B 106:5527–5535
54. Gouda H, Kuntz ID, Case DA et al (2003)
Free energy calculations for theophylline binding to an RNA aptamer: comparison of
MM-PBSA and thermodynamic integration
methods. Biopolymers 68:16–34
55. Hu G, Ma A, Wang J (2017) Ligand selectivity
mechanism and conformational changes in
guanine Riboswitch by molecular dynamics
simulations and free energy calculations. J
Chem Inf Model 57:918–928
56. Hou T, Chen K, McLaughlin WA et al (2006)
Computational analysis and prediction of the
binding motif and protein interacting partners
255
of the Abl SH3 domain. PLoS Comput Biol 2:
e1–e1
57. Stoica I, Sadiq SK, Coveney PV (2008) Rapid
and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. J
Am Chem Soc 130:2639–2648
58. Venken T, Krnavek D, Münch J et al (2011) An
optimized MM/PBSA virtual screening
approach applied to an HIV-1 gp41 fusion
peptide inhibitor. Proteins 79:3221–3235
59. Spiliotopoulos D, Spitaleri A, Musco G (2012)
Exploring PHD fingers and H3K4me0 interactions with molecular dynamics simulations
and binding free energy calculations: AIREPHD1, a comparative study. PLoS One 7:
e46902
60. Opuu V, Sun YJ, Hou T et al (2020) A physicsbased energy function allows the computational redesign of a PDZ domain. Sci Rep
10:11150
61. Simonson T (2003) Electrostatics and dynamics of proteins. Rep Prog Phys 66:737–737
Chapter 15
Mechanoregulation of PDZ Proteins, An Emerging Function
Elsa Bazellières and André Le Bivic
Abstract
Mechanical forces have emerged as essential regulators of cell organization, proliferation, migration, and
polarity to regulate cellular and tissue homeostasis. Changes in forces or loss of the cellular response to them
can result in abnormal embryonic development and diseases. Over the past two decades, many efforts have
been put in deciphering the molecular mechanisms that convert forces into biochemical signals, allowing
for the identification of many mechanotransducer proteins. Here we discuss how PDZ proteins are
emerging as new mechanotransducer proteins by altering their conformations or localizations upon force
loads, leading to the formation of macromolecular modules tethering the cell membrane to the actin
cytoskeleton.
Key words PDZ proteins, Forces, Tight junctions, Adherens junctions, Actomyosin
1
A Brief Introduction on Mechanotransduction/Mechanoregulation in Biology
All the cells and tissues of the body are subject to external and
internal forces. These forces can affect the shape and intracellular
organization of cells, their proliferation, their migration, and their
intercellular interactions. Forces influence the development of
embryos [1] as well as cell functions and homeostasis in the adult
[2]. Moreover, many disease states are characterized by changes in
these forces and/or a loss of the normal cellular response to them
[3]. Over the last decade, mechanotransduction has emerged as a
key process in development and diseases. Mechanotransduction can
be defined as a cellular event that converts a mechanical input such
as fluid shear stress (blood vessels), stretch (lung, intestine),
osmotic forces (urinary tract), mechanical load (bone, muscle)
[4, 5] as well as the impact of the stiffness of the extracellular matrix
(ECM) that surrounds most cells leading to a biochemical response
[6]. In well-studied examples of mechanotransduction, proteins
can undergo force-induced changes into conformations that lead
to modification in affinity for binding partners or catalytic activity.
The mechanical load triggers biochemical changes that can
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_15, © Springer Science+Business Media, LLC, part of Springer Nature 2021
257
258
Elsa Bazellières and André Le Bivic
propagate via the activation of specific signaling pathways [7]. In a
simple context, the mechanical load can accelerate the association
or dissociation of protein–ligand bonds [8]. In the case of adhesion
proteins, that exhibit catch-bond behavior, mechanical loads can
produce changes in the conformation of the proteins that lead to
high affinity for a binding partner [9–11].
Deciphering how fundamental physical principles are controlled by protein interactions is central to understand how forces
are converted into biochemical signals. Elucidation of the specificity, selectivity, and regulatory mechanisms involved in protein–protein interactions can therefore provide important insights into
many biological processes such as cell proliferation, cell migration,
and cell polarity. Structural studies have revealed that mechanosensitive proteins with multiple domains and flexible interdomain
interfaces can pass through multiple conformations [12, 13]. For
example, a bent conformation can open up to a straighter arrangement of domains along the direction of the applied force. These
changes in conformation result in modification of the affinity for
the binding partners, exposure or hiding of different catalytic
domains that will trigger differential intracellular signaling
responses or even redistribution of the protein within the cell
[14–16]. Among the proteins that mediate protein–protein interactions, there is the large PDZ (postsynaptic density 95/Disc
large/Zonula occludens) family. Most of the PDZ proteins are
multimodular scaffold proteins and often contain multiple PDZ
domains which can interact with various binding partners and
thereby assemble supramolecular signaling complexes [17].
As PDZ domains interact with motifs present in many proteins,
understanding the regulatory mechanism of PDZ mediated interactions is important to gain insight into biological processes. So far,
posttranslational modifications, autoinhibition, and allosteric interactions have been proposed to regulate PDZ-mediated interactions
and thus intracellular signaling [18], but little is known about the
impact of mechanical inputs on PDZ proteins. In this review, we
will focus on the current knowledge on the mechanoregulation of
PDZ proteins, focusing on few recent findings.
2
PDZ Proteins as Mechanotransducers
Biological mechanotransducers can be defined as a single protein or
a protein complex that produce or enable a chemical signal in
response to mechanical stimuli. These mechanotransducers can
participate in mechanoreception and mechanotransmission as
direct mechanosensitive structure, which respond by altering their
conformation upon force loads, or as second line mechanotransducers. Among the mechanotransducer proteins studied so far, the
PDZ protein family is emerging as a new pool of protein sensitive to
Mechanoregulation of PDZ Proteins
259
forces, but only few examples are known so far. In epithelial cells,
the adhesion machineries at the cell–cell or cell–substrate interfaces
are known to play an important role in mechanotransduction
[10, 19]. These machineries are composed of several proteins,
and many of them are part of the PDZ family [20]. Being
integrated in a complex modular array tethering the cell membrane
to the cell cytoskeleton, the adhesion scaffolding PDZ proteins
might constitute an important link to transduce the mechanical
signals into an appropriate cell response to maintain cellular
homeostasis. We will thus focus on PDZ proteins from the adhesion machinery that have been recently shown to be regulated by
forces in a direct or indirect manner (Fig. 1).
3
Force Regulated PDZ Proteins
3.1
ZO1
ZO1 is a tight junction scaffolding protein that belongs to the
MAGUK (membrane associated guanylated kinase homolog) family. It is characterized by the occurrence of three PDZ, one SH3 and
a GUK (guanylate kinase homologous domain) domains
[21]. ZO1 is a cytoplasmic protein, that anchors actin filaments
through its actin binding domain at its C terminal region
[22, 23]. At the N-terminus, the PDZ1 and PDZ3 domains bind
to the tight junction membrane associated proteins, Claudins and
JAM-A, and to TAZ, a member of Hippo pathway, while the PDZ2
domain promotes heterodimerization between ZO1 and either
ZO2 or ZO3 [22, 24–26]. A larger region encompassing the
PDZ3, SH3, U5, and GUK domains (ZPSG-1) interacts with and
recruits to junctions the transmembrane TJ protein occludin, but
also DbpA/ZONAB (DNA binding protein A/ZO1 associated
Nucleic Acid-binding protein) [27, 28]. The sequestration of
DbpA by ZO1 and ZO2 at the junctions of confluent monolayers
inhibits its nuclear activity that regulates gene expression and cell
proliferation [27–30].
Recently, Spadaro et al. demonstrated that ZO1 stretches upon
mechanical forces [31]. In 2011, Then et al. have already proposed
an impact of membrane tension on the localization of ZO1
[32]. Under hyperosmotic stress that generates an increase in
membrane tension, the actin cytoskeleton is reorganized with the
appearance of a dense F-actin cortical ring. In this condition, ZO1
expression is increased, and it colocalizes with the newly formed
actin ring [32]. As ZO1 is anchored to the actin filaments through
its C-terminal region, this change in actin organization exerts direct
pulling forces on the ZO1 protein. When present as a heterodimer
with ZO2 within cells, ZO1 is in a stretched configuration that
allows binding to DpbA. However, the depletion of ZO2 together
with an inhibition of myosin contractility promotes a folding of the
N-terminal and C-terminal end of ZO-1, and releases the
260
Elsa Bazellières and André Le Bivic
Fig. 1 Hypothetical role of the PDZ protein in the strengthening and stabilization of intercellular adhesions. (a)
At the level of the tight junctions (TJs), under low tension the mechanotransducer ZO1 is in an inactive
Mechanoregulation of PDZ Proteins
261
interaction with DpbA and Occludin. By applying linearly increasing forces to purified full-length single ZO1 molecules with magnetic tweezers, Spadaro et al. could demonstrate that both the
C-terminal region and the ZPSG-1 module of ZO1 that comprises
the PDZ3, SH3, U5, and GUK domains unfold at forces ranging
from 5 to 20 pN, releasing the autoinhibition interaction between
ZPSG and C terminal domain of ZO1 [31]. ZPSG-1 domain is not
only important for the junctional localization of ZO1 but also for
its interaction with Occludin, DbpA, and thus for barrier formation
and epithelial polarization [33–38]. Forces act as an allosteric effector by stretching ZO1 protein to promote the interaction of ZPSG1 with its ligands, occludin and DbpA. While the stretched conformation of ZO1 is the active conformation, the folded conformation
is the inactive form in which the ZPSG domain is autoinhibited. In
its inactive form, junctional ZO1 remains anchored to the membrane through binding of its N-terminal domain with interactors
such as TAZ or Claudins, while the C-terminal half is intramolecularly autoinhibited. The release of ZPSG-1 domains may also regulate the interaction of other proteins such as α-Catenin, Afadin,
JAM-A, Vinculin and Shroom2 [39].
3.2
MUPP1
MUPP1 belongs to the family of multi-PDZ proteins and contains
a L27 domain at its N-terminal region followed by 13 PDZ
domains [40]. MUPP1 is a structural paralog of PATJ, and both
share several binding partners such as PALS1, PAR-6, AMOT,
Jeap, ZO3, Claudins, or Nectins [41]. Lanaspa et al. by the use of
osmolarity changes demonstrated that both acute and chronic
hyperosmolarity in inner medullary collecting duct 3 cells induce
an increase in the expression of MUPP1, ZO1, and Afadin [42]. As
MUPP1 expression increases, it localizes to the apical side of the
membrane at the level of the Tight Junctions (TJs). To survive
hyperosmotic stresses and to maintain the integrity of the cell
sheet with efficient barrier functions, cells have to adapt through
ä
Fig. 1 (continued) conformation, and the second line mechanotransducers Afadin and Mupp1 are in the
cytoplasm. The actin filaments are exerting low mechanical loads. Under high tension, generated by
actomyosin bundles, ZO1 unfolds and unmasks several domains that leads to the binding with Occludin
and JAM together with the recruitment of DbpA and Afadin. Afadin is then able to recruit Rap2c generating a
positive feedback loop on RHO allowing for actin contractions and thus force generation. This increase in
forces results in the recruitment of Mupp1 at the TJs, that will bind to ZO1, ZO2, and JAM but also potentially
to the CRB3A/Pals1 polarity complex. The formation of this macromolecular complex that tethers transmembrane proteins (Claudins, Occludins, JAM) to the actin cytoskeleton is key for the strengthening and the
stabilization of the TJs. (b) In the lateral domain of the cells, at the level of the lateral Adherens Junctions
(LAJs), the actin filaments that binds to the E-cadherin–catenin complex, exert low tension and SCRIBBLE is in
the cytoplasm. Above the lateral junctions, the E-cadherin–catenin complex is link to vinculin and binds to
actomyosin bundles that are under high mechanical loads. SCRIBBLE is also enriched at the AJ level, and
plays a key role in the stabilization of the AJs
262
Elsa Bazellières and André Le Bivic
reorganization of the actin cytoskeleton as mentioned previously
but also through differential expression of proteins, that move to
the junctions to counterbalance the changes in membrane tension.
Depletion of MUPP1, results in a disruption of the TJ barrier, with
a drop of transepithelial resistance of about 25% [42]. Molecularly,
depletion of MUPP1, in hyperosmotic conditions, triggers a
decreased expression and loss of membrane localization of Claudin4, a TJ protein [42]. These data clearly show that MUPP1 is
important in the maintenance of the epithelial homeostasis and
confirm previous study that pointed out a predominant role of
MUPP1 in the disruption of both TJs barrier and apicobasal polarity [43–45]. A more recent study shows that MUPP1 stability and
degradation in endothelial cells depends on the regulation of
PDZRN3, an E3 ubiquitin ligase PDZ domain containing ring
finger 3 protein [46]. The interactions between PDZRN3,
MUPP1, PAR3, and aPKC regulate the stabilization of TJs in
endothelial cells. Perturbation of PDZRN3 expression induces
degradation of MUPP1 that correlates with destabilization of the
actin cytoskeleton and disruption of the TJs. All these studies point
toward a role of MUPP1 in the stabilization and maintenance of
actin cytoskeleton and TJs in cells.
20 years ago, it was suggested that MUPP1 could change its
conformation through the interaction of its PDZ10 domain with
the 5-hydroxytryptamine type 2C receptors [47]. This interaction
induces a conformational change in MUPP1 and triggers a clustering of the 5-hydroxytryptamine type 2C receptors at the cell membrane, triggering downstream signal transduction pathways. It is of
interest that different PDZ domains of MUPP1 can bind to
CADM1, a transmembrane cell adhesion protein, with different
affinities. The PDZ2 of MUPP1, for example, presents the highest
affinity for CADM1, but when this PDZ is absent, CADM1 can still
interact with other PDZ domains of MUPP1. These data could
point to a change in conformation of MUPP1 that would unmask
the PDZ2 domain leading to a strong binding to CADM1
[48, 49]. In the case of CADM1 and maybe of other interactors
of MUPP1, the interactions with multiple domains can be an
alternative when some domains are either occupied by other ligands
or hidden by an autoinhibitory conformation of MUPP1. These
multi–low-affinity interactions might then serve as transient interaction contacts before MUPP1 reaches its stretched configuration.
However, the unfolding/folding regulation of MUPP1 is still
under debate as only indirect evidence has been obtained to date.
3.3
PAR3
PAR3 is part of the Partitioning defective proteins and presents a
CR1 oligomerization domain, 3 PDZ domains and aPKC binding
domain. A recent study pointed out that cortical forces are responsible for the clustering of PAR3 on the cell cortex. In C. elegans,
inhibition of actin contraction with blebbistatin or actin filament
Mechanoregulation of PDZ Proteins
263
polymerization causes a reduction of the PAR3 clusters on the
cortex, demonstrating that higher cortical tension can drive
PAR3 cluster formation at the cortex [50]. The actomyosin membrane associated network generates flows [51], which promote
symmetry breaking along the anterior posterior axis through the
advection of polarity components [52–54]. The polarized actomyosin contractions pull actin networks along the membrane toward
the anterior region while triggering local disassembly and turnover
via increased local tension, resulting in flow of material [55]. Based
on the advective flow model proposed by Goerhing et al. [53] that
predicts that diffusivity and turnover of cortical polarity proteins
should be slow and stable enough to be passively transport by the
advective cortical flow, Wang et al. [50] postulate a “clustering and
stabilization” hypothesis for PAR3. By using fluorescence recovery
after photobleaching, they demonstrate that the half-life of PAR3
and PKC are shorter before symmetry breaking. During the early to
middle establishment phase, when the clusters are formed, the halflife increases. Molecularly, during this phase the activity of Cdc42 is
reduced allowing PKC3 and PAR6 to associate with PAR3 clusters
at the cortex and thus facilitates effective transport by advective
cortical flows. During the maintenance phase, the half-lives of
PAR3 and PKC are shorter, when the clusters disassemble, concomitant with an increased activity of Cdc42 that prevents PKC and
PAR6 association with PAR3. As cortical tensions trigger a dynamic
equilibrium between an unclustered and clustered form of PAR3
correlating with its association with PAR6 and PKC, it is tempting
to speculate that PAR3 may undergo structural changes by
mechanical stretching, which could relieve the amino-terminal
CR1 domain from intramolecular inhibitions, thus promoting
PAR3 oligomerization [56]. These mechanically induced conformational changes may directly activate the ability of the CR1
domain to facilitate its oligomerization, and proper localization in
cells [56]. Furthermore, similar cortical flows are observed during
cell division or cell migration, and then could, if proven, also
explain the interaction between the different members of the PAR
complex.
4
PDZ Proteins “Second Line” Mechanotransducers
In this part, we will describe how some PDZ proteins interact with
proteins that are activated upon forces. These interactions may
trigger a change in the conformation or in the localization of the
“second line” mechanotransducers, thereby regulating different
signal pathways.
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Elsa Bazellières and André Le Bivic
4.1
PAR6
PAR6 is part of the Partitioning defective proteins in C. elegans, and
encodes a protein with PKC binding domain (PB1), a semiCdc42/Rac1 interactive binding (CRIB) and a PDZ domain
[57]. Recently a PDZ binding motif (PBM) was identified at its
C-terminal region [58]. PAR6 in many species is involved in cell
division, cell polarization, and cell migration, processes that are
characterized by changes in cellular forces [59–61].
PAR6 function is able to segregate to the apical or leading edge
of epithelial cells upon activation by Cdc42-GTP [57, 62]. Accumulating evidence indicates that Cdc42 responds to and is activated
upon different mechanical loads such as hyperosmolarity, shear
stress, and intercellular increased tension [63]. Upon exposure to
mechanical loads, Cdc42 translocates from cytosol to the membrane and is concomitantly activated [64]. Activation of Cdc42 by
exchange of GDP for GTP triggers actin polymerization and generation of tension [65, 66]. Binding of activated Cdc42-GTP to the
CRIB domain of PAR6 favors the interaction with CRUMBS over
the interaction with PALS1 through a change in conformation of
the CRIB-PDZ domains of PAR6 [67]. Upon Cdc42-GTP binding, a portion of the flexible CRIB motif folds into a stable conformation with the PDZ domain [68], triggering an increase affinity
for the interaction with CRUMBS, and releasing the interaction
with PALS1. The modular nature of PAR6 may allow the mutually
exclusive interaction of CRUMBS and PALS1 with its PDZ
domains, regulating the assembly and localization of different
polarity complexes depending on the cellular context.
The localization of PAR6 to the plasma membrane depends on
both the PDZ and the PBM domains, as both deletions caused a
strong mislocalization of PAR6 to the cytosol. The PBM domain
binds with different affinities to the PDZ1 and PDZ3 of PAR3,
whose clustering is mediated by an increase in cortical tension as
previously mentioned [50]. The weak but multivalent interaction
of one PAR3 molecule with two PAR6 molecules might allow the
assembly a large cluster of PAR complexes at the cell membrane.
The formation of the large-scale cluster of PAR complexes can
facilitate the actomyosin dependent advective transport of several
PAR6 proteins during the establishment of polarity but also the
formation of large clusters made with several proteins such as
aPKC, PALS1, or CRUMBS that can serve different functions.
4.2
DLG
DLG is a MAGUK protein that presents an L27 domain, 3 PDZ
domains, an SH3 domain, a HOOK domain and a Guanylate like
domain. DLG is a member of the basolateral polarity complex
SCRIBBLE, and is involved in processes such as cell division, cell
migration and cell polarity [69] The N-terminal L27 domain interacts with the two L27 domains of calcium/calmodulin-dependent
serine protein kinase (CASK) [70]. CASK is a membrane-associated
guanylate kinase and a scaffolding protein, that has been
Mechanoregulation of PDZ Proteins
265
demonstrated to recruit or organize other proteins at the plasma
membrane to coordinate signal transduction pathways within the
cytoplasm and nucleus [71]. A study done in 2016, using a cell
mechanical stretch device shows that CASK expression and localization to the basal membrane is needed for the inhibition of
proliferation of cells under cyclic stretch [72]. The authors show
that CASK interacts with ß1-integrin, however, the tension-driven
interaction between these two proteins still has to be formally
proven. Depletion of CASK results in aberrant proliferation of
cells under mechanical stress demonstrating that CASK localization
under tension is important for the mechanoregulation of the cell
division [72]. In 2019, Porter et al. demonstrated that the direct
interaction of CASK with DLG is required for normal cortical
recruitment of NUMA, a key component of spindle orientation
machinery [73]. Disruption of this interaction affects the integrity
of epithelial architecture and results in misoriented cell division that
give rise to multilumen cyst in 3D. Since the formation of 3D cysts
generates an increase in tension at the cell–cell interface [74], it is
tempting to speculate that CASK is recruited at the cell–cell interface in a force-dependent manner and then will recruit DLG allowing the formation of well polarized 3D cysts.
DLG interacts with the C terminal PDZ binding domain of
CD97 and together these proteins are part of the adherens junctional signaling complex composed of E-cadherin and catenins
[75]. This macromolecular complex is linked to the F actin cortex
and is thus submitted to cellular forces. The localization of CD97 at
the plasma membrane is actin dependent as blocking actin polymerization and elongation prevents its membrane localization. When
present at the cell membrane CD97 strengthens the adherens
junction (AJs) [76] while deletion of its PDZ binding domain
results in the loss of cell–cell contacts [77] upon mechanical shear
stress. Taken together these data show a role of CD97 in the
mechanoregulation of cell–cell contacts. In a recent study, mechanical stimulation of epithelial cells applied by using shear stress or
wound assay results in a rapid phosphorylation of Ser740 of CD97.
This phosphorylation disrupts the binding of DLG1 to the PDZ
binding domain of CD97, and correlates with a disorganization of
the actin cytoskeleton. PKC contributes to the mechanical force
induced cellular responses [78] and is a potential candidate to
phosphorylate CD97 at S740. PKCα interacts via its PBM with
PDZ3 domain of DLG1 [79]. Upon mechanical stress, PKCα
might be recruited to the cell membrane via DLG1 and will then
trigger phosphorylation of CD97 causing F-actin depolymerization, loss of cell–cell contact, and DLG1 detachment. This signaling pathway when activated induces depolymerization of actin and
loss of cell–cell contacts, that will result in a relaxation of the
tension at this particular cellular junction avoiding tissue breaking.
266
4.3
Elsa Bazellières and André Le Bivic
Afadin
Afadin is a filamentous actin binding protein with two Ras domains,
a forkhead association domain, a dilute domain, a PDZ domain,
and three proline-rich domains. Afadin is implicated in many cellular processes from cell survival, cell proliferation to cell migration
and formation of the apical junctions in epithelial cells [80–83]. Afadin can interact both directly or indirectly with the actin cytoskeleton through several partners such as JAM-A, ZO1/ZO2, vinculin,
and α-actinin [84–88]. Afadin is thus a strong candidate to be
involved in mechanoregulated processes as many of its partners
have been already linked to mechanotransduction. Interestingly,
depletion of Afadin, JAM-A or double depletion of ZO1/ZO2
results in similar phenotypes with increased contractility triggered
by activation of RhoA and phosphorylation of myosin-light chain
[89–91]. In response to ZO1/ZO2 double depletion, Afadin is
recruited to the cellular cell contacts, and inhibiting contractility
perturbs the homogeneous localization of Afadin. Thus, myosin
contractility is essential for maintaining Afadin uniform distribution
along the zonula adherens (ZA). Removing Afadin in ZO1/ZO2
depleted cells specifically altered actomyosin architecture at the ZA
of tricellular junctions where actin cables are anchored. This perturbation is accompanied by discontinuities in the E-cadherin,
claudin, and occludin stainings [92]. Taken together, this data
shows a synergy between ZO and Afadin depletions in disrupting
tissue integrity under tension. Afadin may therefore act as a robust
protein scaffold that maintains ZA architecture at tricellular
junctions.
JAM-A, a component of the TJs, via its PDZ binding motif, can
associate with signaling molecules such as scaffold PDZ proteins,
ZO1/2, and Afadin, as well as with the guanine exchange factor
PDZ-GEF2 [83, 93]. Monteiro et al. demonstrate that JAM-A
interacts with Afadin, ZO2, and PDZ-GEF1 to activate the small
GTPase Rap2c [89]. The activation of Rap2c controls the contraction of the apical cytoskeleton regulating the epithelial permeability
to prevent cell injury. In this study, the authors also mention that
Afadin is able to immunoprecipitate a doublet of JAM-A that might
represent phosphorylated forms of JAM-A, although they did not
investigate this phosphorylation but another team did. Scott at al
show that mechanical stimulation can trigger phosphorylation of
JAM-A [94]. Forces were applied by using fluid shear stress or by
using magnetic tweezer. After applying forces, several biochemical
analyses were performed that demonstrate that tension induces
rapid phosphorylation of JAM-A S284. This phosphorylation of
JAM-A induces activation of RhoA by PKCζ triggering cell stiffening by modifying actin cytoskeleton. These results clearly demonstrate that JAM-A is a direct transducer of mechanical forces.
Interestingly, when JAM-A is localized at the TJs which are under
high levels of tension [95] it is phosphorylated [96]. It is thus
tempting to speculate that mechanical tension, above a certain
Mechanoregulation of PDZ Proteins
267
threshold might induce the phosphorylation of JAM-A. This phosphorylation will control the affinity for Afadin thereby controlling
the contraction of apical cytoskeleton that allows for the maintenance of ZA and TJs.
4.4
SCRIBBLE
SCRIBBLE is a classical multimodular scaffold protein that contains 16 leucine-rich repeats (LRR) and four PDZ domains.
N-terminal LRR domain can associate with LGL and is required
for the association with the lateral cortex and the establishment of
apicobasal polarity [97, 98]. The C-terminal PDZ domains of
SCRIBBLE can interact with a diverse range of proteins such as
β-Pix [99], Vangl2 [100], and LGN [101], allowing for cell polarization, cell migration [102], establishment of planar cell polarity,
and cell division, respectively. In epithelial cells, SCRIBBLE is
required for normal intercellular adhesion by stabilizing E-cadherin–p120 catenin at the plasma membrane [103]. 10 years ago,
E-cadherin complex was tagged as a mechanosensor complex. Since
then many studies using different approaches such as FRET sensor,
magnetic tweezer, and cell stretching have confirmed that
E-cadherin can sense changes in mechanical forces within an epithelial monolayer [104–106]. The role of SCRIBBLE in mechanotransduction has, however, not been investigated so far. Some
hints based on the function of SCRIBBLE in the stabilization of
E-cadherin point to a role in the mechanoregulation of AJs.
E-cadherin receptors form adhesive clusters that are coupled to
the contractile actomyosin cortex [107, 108]. At the cellular level,
E-cadherin adhesion not only binds cells together but also mechanically couples the contractile modules of neighboring cells together
to generate junctional tension [109]. This junctional tension has
been mainly treated as homogeneous along the cell–cell interface.
However, E-cadherin forms clusters of different sizes along the
junctions between cells [108–110]. Interestingly, despite overall
basolateral localization, SCRIBBLE is enriched at the ZAs, where
it stabilizes E-cadherin–p120 catenin adhesions [111, 112]. At the
level of the ZAs, clusters of stabilized E-cadherin are linked to large
actomyosin bundles [113–115]. Below the AJs, at the lateral adherens junctions (LAJs), E-cadherin is coupled to less aligned actomyosin network. Contractile tension is thereby greater at the ZA
than at the LAJ. SCRIBBLE could be enriched at the ZA in a
tension-dependent manner, and thereby by stabilizing E-cadherin
could be implicated in a feedback positive loop in the generation of
forces at the ZAs. This tension driven localization of SCRIBBLE to
E-cadherin can also take place during the orientation of the mitotic
spindle to align epithelial cell division. In 2017, Hart et al., by
applying uniaxial stretch on epithelial cells, showed that cell division
aligned with the stretch axis [116]. The orientation of this cell
division axis requires trans-engagement of E-cadherin adhesions
and involves tension-dependent recruitment of LGN to
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Elsa Bazellières and André Le Bivic
E-cadherin. LGN can directly interact with the cytosolic tail of
E-cadherin that localizes LGN at the cell–cell contacts
[117]. Increase in tension does not trigger a polarized distribution
of E-cadherin, while LGN and myosin IIA are polarized suggesting
that an additional intermediate, LGN interacting protein might be
involved in its polarized localization. This protein could be SCRIBBLE as other ones (DLG, Afadin, ERM proteins) were ruled out in
this study. SCRIBBLE is indeed able to interact with LGN, and the
interaction between LGN and E-cadherin is reduced in cells
depleted for SCRIBBLE. The formation of this ternary complex
is also important for proper cell division [101]. Taken together, all
these data point to SCRIBBLE as a likely “second line” mechanotransducer to trigger signaling pathways downstream of the
mechanosensitive E-cadherin units.
5
Concluding Remarks
In the last two decades, mechanical stimuli have emerged as essential regulators of several biological processes. Mechanotransduction
has revealed a new layer of control of the interaction between
proteins, and will potentially lead to global guiding principles for
the organization of complex living systems. The convergent interests from physicists and biologists to understand the complexity of
integrated systems have led to the development of new biophysical
tools. These new technological advances such as stretching devices,
optical/magnetic tweezers, or atomic force microscopy have
enabled to measure and apply controlled forces on cells. Applying
different forces on cells trigger several cellular responses and activate different signaling pathways that have led to the identification
of an emerging mechanotransducing function for PDZ proteins.
These mechanotransducers can participate in mechanoreception
and mechanotransmission as direct mechanosensitive components
or as second line mechanotransducers.
Pulling forces generated by the actomyosin network can stretch
molecules exposing their cryptic binding sites or cryptic phosphorylation sites, triggering specific signaling pathways [5, 118]. In the
case of ZO1, physical forces are responsible for the stretching of the
molecule that is needed for its correct localization while allowing
for the interaction with its different partners. This activated
unfolded ZO1 protein is important for the maintenance of the
TJs. The actomyosin network can thus directly by its contractions
pull on proteins but also on the actin cytoskeleton, resulting in flow
of material that leads to the formation of polarized clusters of
proteins at the cell cortex that allow for adaptation of cell cytoskeleton [51, 119]. Under higher cortical tension PAR3 clusters and
MUPP1 is recruited to the cell cortex. The tension-dependent
clustering of PAR3 could promote its oligomerization at the cell
Mechanoregulation of PDZ Proteins
269
cortex where it could recruit PAR6 and aPKC, leading to the
establishment of anteroposterior or apicobasal polarity. The recruitment of the PDZ protein MUPP1 to the TJs is key to maintaining
the integrity of the TJ barrier during hyperosmotic stress. Finally,
loading forces can regulate cell adhesion machineries [105, 106,
120]. The adhesive contacts can adapt to external forces, by modifying their binding to specific proteins, their structural conformations, their stability but also their links to the actin cytoskeleton.
Some PDZ proteins such as SCRIBBLE, Afadin, or ZO1 can, upon
loading forces, strengthen the cell adhesion machinery at the ZA
and TJs localization respectively to preserve epithelial homeostasis
by allowing for the proper polarization of the tissue and the tethering of the adhesions to the actin cytoskeleton. In these cases, the
connections between adhesions are strengthened by assembly of
new components, however in other cases the connections can be
severed [121]. DLG is located at cell–cell contacts and upon forces
generation will recruit aPKC at the ZA. The recruitment of aPKC
by DLG at the ZA can then regulate the load of tension at the cell–
cell contact by controlling the depolymerization of actin and disruption of cell–cell contact leading to a relaxation of the tissue
when the forces are too elevated.
The active nature of cell adhesion machineries implies that the
time from the mechanosensing to the mechanoresponse is slow and
occurs within seconds to minutes allowing the occurrence of many
different interactions between proteins [122–124]. In the case of
PDZ scaffolds proteins, this could trigger specific interactions
allowing for the cells to have a multimodal adaptive “repertoire”
to properly adapt their responses to the change in forces.
Acknowledgments
Elsa Bazellières and André Le Bivic are supported by CNRS. This
project was developed in the context of the LabEx INFORM
(ANR-11-LABX-0054) and of the A*MIDEX project (ANR-11IDEX-0001-02) funded by the “Investissements d’Avenir” French
Government program.
References
1. Petridou NI, Heisenberg C (2019) Tissue
rheology in embryonic organization. EMBO
J 38:e102497
2. Salvi AM, DeMali KA (2018) Mechanisms
linking mechanotransduction and cell metabolism. Curr Opin Cell Biol 54:114–120
3. Jaalouk DE, Lammerding J (2009) Mechanotransduction gone awry. Nat Rev Mol Cell
Biol 10:63–73
4. Sidhwani P, Yelon D (2019) Fluid forces
shape the embryonic heart: insights from zebrafish. Curr Top Dev Biol 132:395–416
5. Pruitt BL, Dunn AR, Weis WI, Nelson WJ
(2014) Mechano-transduction: from molecules to tissues. PLoS Biol 12:e1001996
6. Dzamba BJ, DeSimone DW (2018) Extracellular matrix (ECM) and the sculpting of
270
Elsa Bazellières and André Le Bivic
embryonic tissues. Curr Top Dev Biol
130:245–274
7. Hoffman BD, Grashoff C, Schwartz MA
(2011) Dynamic molecular processes mediate
cellular
mechanotransduction.
Nature
475:316–323
8. Depoil D, Dustin ML (2014) Force and affinity in ligand discrimination by the TCR.
Trends Immunol 35:597–603
9. Chen W, Lou J, Evans EA, Zhu C (2012)
Observing force-regulated conformational
changes and ligand dissociation from a single
integrin on cells. J Cell Biol 199:497–512
10. Hu X, Margadant FM, Yao M, Sheetz MP
(2017) Molecular stretching modulates
mechanosensing pathways: molecular stretching modulates mechanosensing pathways.
Protein Sci 26:1337–1351
11. Pannekoek W-J, de Rooij J, Gloerich M
(2019) Force transduction by cadherin adhesions in morphogenesis. F1000Res 8:1044
12. Cailliez F, Lavery R (2005) Cadherin
mechanics and complexation: the importance
of calcium binding. Biophys J 89:3895–3903
13. Springer TA, Dustin ML (2012) Integrin
inside-out signaling and the immunological
synapse. Curr Opin Cell Biol 24:107–115
14. Rutherford SL, Zuker CS (1994) Protein
folding and the regulation of signaling pathways. Cell 79:1129–1132
15. Papagrigoriou E, Gingras AR, Barsukov IL,
Bate N, Fillingham IJ, Patel B, Frank R, Ziegler WH, Roberts GC, Critchley DR, Emsley J
(2004) Activation of a vinculin-binding site in
the talin rod involves rearrangement of a fivehelix bundle. EMBO J 23:2942–2951
16. Smith ML, Gourdon D, Little WC, Kubow
KE, Eguiluz RA, Luna-Morris S, Vogel V
(2007) Force-induced unfolding of fibronectin in the extracellular matrix of living cells.
PLoS Biol 5:e268
17. Manjunath GP, Ramanujam PL, Galande S
(2018) Structure function relations in PDZdomain-containing proteins: implications for
protein networks in cellular signalling. J Biosci
43:155–171
18. Lee H-J, Zheng JJ (2010) PDZ domains and
their binding partners: structure, specificity,
and modification. Cell Commun Signal 8:8
19. Charras G, Yap AS (2018) Tensile forces and
mechanotransduction at cell–cell junctions.
Curr Biol 28:R445–R457
20. Heinemann U, Schuetz A (2019) Structural
features of tight-junction proteins. Int J Mol
Sci 20:6020
21. Stevenson BR, Siliciano JD, Mooseker MS,
Goodenough DA (1986) Identification of
ZO-1: a high molecular weight polypeptide
associated with the tight junction (zonula
occludens) in a variety of epithelia. J Cell
Biol 103:755–766
22. Fanning AS, Jameson BJ, Jesaitis LA, Anderson JM (1998) The tight junction protein
ZO-1 establishes a link between the transmembrane protein occludin and the actin
cytoskeleton. J Biol Chem 273:29745–29753
23. Itoh M, Nagafuchi A, Moroi S, Tsukita S
(1997) Involvement of ZO-1 in cadherinbased cell adhesion through its direct binding
to α catenin and actin filaments. J Cell Biol
138:181–192
24. Itoh M, Furuse M, Morita K, Kubota K,
Saitou M, Tsukita S (1999) Direct binding
of three tight junction-associated Maguks,
Zo-1, Zo-2, and Zo-3, with the Cooh termini
of claudins. J Cell Biol 147:1351–1363
25. Wittchen ES, Haskins J, Stevenson BR (1999)
Protein interactions at the tight junction.
Actin has multiple binding partners, and
ZO-1 forms independent complexes with
ZO-2
and
ZO-3.
J
Biol
Chem
274:35179–35185
26. Remue E, Meerschaert K, Oka T,
Boucherie C, Vandekerckhove J, Sudol M,
Gettemans J (2010) TAZ interacts with
zonula occludens-1 and -2 proteins in a
PDZ-1 dependent manner. FEBS Lett
584:4175–4180
27. Balda MS, Matter K (2000) The tight junction protein ZO-1 and an interacting transcription factor regulate ErbB-2 expression.
EMBO J 19:2024–2033
28. Spadaro D, Tapia R, Jond L, Sudol M, Fanning AS, Citi S (2014) ZO proteins redundantly regulate the transcription factor
DbpA/ZONAB.
J
Biol
Chem
289:22500–22511
29. Balda MS, Garrett MD, Matter K (2003) The
ZO-1-associated Y-box factor ZONAB regulates epithelial cell proliferation and cell density. J Cell Biol 160:423–432
30. Sourisseau T, Georgiadis A, Tsapara A, Ali
RR, Pestell R, Matter K, Balda MS (2006)
Regulation of PCNA and cyclin D1 expression and epithelial morphogenesis by the
ZO-1-regulated
transcription
factor
ZONAB/DbpA.
Mol
Cell
Biol
26:2387–2398
31. Spadaro D, Le S, Laroche T, Mean I, Jond L,
Yan J, Citi S (2017) Tension-dependent
stretching activates ZO-1 to control the
Mechanoregulation of PDZ Proteins
junctional localization of its interactors. Curr
Biol 27:3783–3795.e8
32. Then C, Bergler T, Jeblick R, Jung B,
Banas B, Kr€amer BK (2011) Hypertonic stress
promotes the upregulation and phosphorylation of zonula occludens 1. Nephron Physiol
119:p11–p21
33. Fanning AS, Ma TY, Anderson JM (2002)
Isolation and functional characterization of
the actin binding region in the tight junction
protein ZO-1. FASEB J 16:1835–1837
34. Fanning AS, Little BP, Rahner C,
Utepbergenov D, Walther Z, Anderson JM
(2007) The unique-5 and -6 motifs of ZO-1
regulate tight junction strand localization and
scaffolding properties. Mol Biol Cell
18:721–731
35. Umeda K, Ikenouchi J, Katahira-Tayama S,
Furuse K, Sasaki H, Nakayama M, Matsui T,
Tsukita S, Furuse M, Tsukita S (2006) ZO-1
and ZO-2 independently determine where
claudins are polymerized in tight-junction
strand formation. Cell 126:741–754
36. Ikenouchi J, Umeda K, Tsukita S, Furuse M,
Tsukita S (2007) Requirement of ZO-1 for
the formation of belt-like adherens junctions
during epithelial cell polarization. J Cell Biol
176:779–786
37. Rodgers LS, Beam MT, Anderson JM, Fanning AS (2013) Epithelial barrier assembly
requires coordinated activity of multiple
domains of the tight junction protein ZO-1.
J Cell Sci 126:1565–1575
38. Odenwald MA, Choi W, Buckley A,
Shashikanth N, Joseph NE, Wang Y, Warren
MH, Buschmann MM, Pavlyuk R,
Hildebrand J, Margolis B, Fanning AS,
Turner JR (2017) ZO-1 interactions with
F-actin and occludin direct epithelial polarization and single lumen specification in 3D culture. J Cell Sci 130:243–259
39. Fanning AS, Anderson JM (2009) Zonula
occludens-1 and -2 are cytosolic scaffolds
that regulate the assembly of cellular junctions. Ann N Y Acad Sci 1165:113–120
40. Ullmer C, Schmuck K, Figge A, Lübbert H
(1998) Cloning and characterization of
MUPP1, a novel PDZ domain protein.
FEBS Lett 424:63–68
41. Assémat E, Bazellières E, Pallesi-Pocachard E,
Le Bivic A, Massey-Harroche D (2008) Polarity complex proteins. Biochim Biophys Acta
1778:614–630
42. Lanaspa M, Andres-Hernando A, Rivard C,
Dai Y, Berl T (2008) Hypertonic stress
increases claudin-4 expression and tight junction integrity in association with MUPP1 in
271
IMCD3 cells. Proc Natl Acad Sci U S A
105:15797–15802
43. Latorre IJ, Roh MH, Frese KK, Weiss RS,
Margolis B, Javier RT (2005) Viral
oncoprotein-induced mislocalization of select
PDZ proteins disrupts tight junctions and
causes polarity defects in epithelial cells. J
Cell Sci 118:4283–4293
44. Shin K, Straight S, Margolis B (2005) PATJ
regulates tight junction formation and polarity in mammalian epithelial cells. J Cell Biol
168:705–711
45. Straight SW, Shin K, Fogg VC, Fan S, Liu C-J,
Roh M, Margolis B (2004) Loss of PALS1
expression leads to tight junction and polarity
defects. Mol Biol Cell 15:1981–1990
46. Sewduth RN, Kovacic H, Jaspard-Vinassa B,
Jecko V, Wavasseur T, Fritsch N, Pernot M,
Jeaningros S, Roux E, Dufourcq P,
Couffinhal T, Duplàa C (2017) PDZRN3
destabilizes endothelial cell-cell junctions
through a PKCζ-containing polarity complex
to increase vascular permeability. Sci Signal
10:464
47. Bécamel C, Figge A, Poliak S, Dumuis A,
Peles E, Bockaert J, Lübbert H, Ullmer C
(2001)
Interaction
of
serotonin
5-hydroxytryptamine type 2C receptors with
PDZ10 of the multi-PDZ domain protein
MUPP1. J Biol Chem 276:12974–12982
48. Fujita E, Tanabe Y, Imhof BA, Momoi MY,
Momoi T (2012) A complex of synaptic adhesion molecule CADM1, a molecule related to
autism spectrum disorder, with MUPP1 in
the cerebellum. J Neurochem 123:886–894
49. Baliova M, Juhasova A, Jursky F (2014) Using
a collection of MUPP1 domains to investigate
the similarities of neurotransmitter transporters C-terminal PDZ motifs. Biochem Biophys Res Commun 454:25–29
50. Wang S-C, Low TYF, Nishimura Y, Gole L,
Yu W, Motegi F (2017) Cortical forces and
CDC-42 control clustering of PAR proteins
for Caenorhabditis elegans embryonic polarization. Nat Cell Biol 19:988–995
51. Munro E, Nance J, Priess JR (2004) Cortical
flows powered by asymmetrical contraction
transport PAR proteins to establish and maintain anterior-posterior polarity in the early
C. elegans embryo. Dev Cell 7:413–424
52. Goehring NW, Hoege C, Grill SW, Hyman
AA (2011) PAR proteins diffuse freely across
the anterior–posterior boundary in polarized
C. elegans embryos. J Cell Biol 193:583–594
53. Goehring NW, Trong PK, Bois JS,
Chowdhury D, Nicola EM, Hyman AA,
Grill SW (2011) Polarization of PAR proteins
272
Elsa Bazellières and André Le Bivic
by advective triggering of a pattern-forming
system. Science 334:1137–1141
54. Mayer M, Depken M, Bois JS, Jülicher F, Grill
SW (2010) Anisotropies in cortical tension
reveal the physical basis of polarizing cortical
flows. Nature 467:617–621
55. McFadden WM, McCall PM, Gardel ML,
Munro EM (2017) Filament turnover tunes
both force generation and dissipation to control long-range flows in a model actomyosin
cortex. PLoS Comput Biol 13:e1005811
56. Benton R, Johnston DS (2003) Drosophila
PAR-1 and 14-3-3 inhibit Bazooka/PAR-3
to establish complementary cortical domains
in polarized cells. Cell 115:691–704
57. Joberty G, Petersen C, Gao L, Macara IG
(2000) The cell-polarity protein Par6 links
Par3 and atypical protein kinase C to Cdc42.
Nat Cell Biol 2:531–539
58. Renschler FA, Bruekner SR, Salomon PL,
Mukherjee A, Kullmann L, Schütz-Stoffregen
MC, Henzler C, Pawson T, Krahn MP, Wiesner S (2018) Structural basis for the interaction between the cell polarity proteins Par3
and Par6. Sci Signal 11:517
59. Uroz M, Wistorf S, Serra-Picamal X, Conte V,
Sales-Pardo M, Roca-Cusachs P, Guimerà R,
Trepat X (2018) Regulation of cell cycle progression by cell-cell and cell-matrix forces.
Nat Cell Biol 20:646–654
60. Ladoux B, Mège R-M, Trepat X (2016)
Front-rear polarization by mechanical cues:
from single cells to tissues. Trends Cell Biol
26:420–433
61. Trepat X, Wasserman MR, Angelini TE,
Millet E, Weitz DA, Butler JP, Fredberg JJ
(2009) Physical forces during collective cell
migration. Nat Phys 5:426–430
62. Etienne-Manneville S, Hall A (2003) Cell
polarity: Par6, aPKC and cytoskeletal crosstalk. Curr Opin Cell Biol 15:67–72
63. Lewis A, Di Ciano C, Rotstein OD, Kapus A
(2002) Osmotic stress activates Rac and
Cdc42 in neutrophils: role in hypertonicityinduced actin polymerization. Am J Phys Cell
Phys 282:C271–C279
64. Li S, Chen BPC, Azuma N, Hu Y-L, Wu SZ,
Sumpio BE, Shyy JY-J, Chien S (1999) Distinct roles for the small GTPases Cdc42 and
Rho in endothelial responses to shear stress. J
Clin Invest 103:1141–1150
65. Tang DD, Gunst SJ (2004) The small GTPase
Cdc42 regulates actin polymerization and
tension development during contractile stimulation of smooth muscle. J Biol Chem
279:51722–51728
66. Plutoni C, Bazellieres E, Le Borgne-RochetM, Comunale F, Brugues A, Séveno M,
Planchon D, Thuault S, Morin N, Bodin S,
Trepat X, Gauthier-Rouvière C (2016)
P-cadherin promotes collective cell migration
via a Cdc42-mediated increase in mechanical
forces. J Cell Biol 212:199–217
67. Whitney DS, Peterson FC, Kittell AW, Egner
JM, Prehoda KE, Volkman BF (2016) Binding of crumbs to the Par-6 CRIB-PDZ module is regulated by Cdc42. Biochemistry
55:1455–1461
68. Garrard SM, Capaldo CT, Gao L, Rosen MK,
Macara IG, Tomchick DR (2003) Structure
of Cdc42 in a complex with the GTPasebinding domain of the cell polarity protein,
Par6. EMBO J 22:1125–1133
69. Su W-H, Mruk DD, Wong EWP, Lui W-Y,
Cheng CY (2012) Polarity protein complex
Scribble/Lgl/Dlg and epithelial cell barriers.
Adv Exp Med Biol 763:149–170
70. Lee S, Fan S, Makarova O, Straight S, Margolis B (2002) A novel and conserved proteinprotein interaction domain of mammalian
Lin-2/CASK binds and recruits SAP97 to
the lateral surface of epithelia. Mol Cell Biol
22:1778–1791
71. Zhu Z-Q, Wang D, Xiang D, Yuan Y-X, Wang
Y (2014) Calcium/calmodulin-dependent
serine protein kinase is involved in exendin4-induced insulin secretion in INS-1 cells.
Metabolism 63:120–126
72. Jiang M, Qiu J, Zhang L, Lü D, Long M,
Chen L, Luo X (2016) Changes in tension
regulates proliferation and migration of fibroblasts by remodeling expression of ECM proteins. Exp Ther Med 12:1542–1550
73. Porter AP, White GRM, Mack NA, Malliri A
(2019) The interaction between CASK and
the tumour suppressor Dlg1 regulates mitotic
spindle orientation in mammalian epithelia. J
Cell Sci 132:jcs230086
74. Hannezo E, Prost J, Joanny J-F (2014) Theory of epithelial sheet morphology in three
dimensions. Proc Natl Acad Sci U S A
111:27–32
75. Takeichi M (2014) Dynamic contacts: rearranging adherens junctions to drive epithelial
remodelling. Nat Rev Mol Cell Biol
15:397–410
76. Becker S, Wandel E, Wobus M, Schneider R,
Amasheh S, Sittig D, Kerner C, Naumann R,
Hamann J, Aust G (2010) Overexpression of
CD97 in intestinal epithelial cells of transgenic mice attenuates colitis by strengthening
adherens junctions. PLoS One 5:e8507
Mechanoregulation of PDZ Proteins
77. Hilbig D, Sittig D, Hoffmann F,
Rothemund S, Warmt E, Quaas M,
Stürmer J, Seiler L, Liebscher I, Hoang NA,
K€as JA, Banks L, Aust G (2018) Mechanodependent
phosphorylation
of
the
PDZ-binding Motif of CD97/ADGRE5
modulates cellular detachment. Cell Rep
24:1986–1995
78. Sivaramakrishnan S, Schneider JL, Sitikov A,
Goldman RD, Ridge KM (2009) Shear stress
induced reorganization of the keratin intermediate filament network requires phosphorylation by protein kinase C ζ. Mol Biol Cell
20:2755–2765
79. O’Neill AK, Gallegos LL, Justilien V, Garcia
EL, Leitges M, Fields A, Hall RA, Newton AC
(2011) PKCα interacts with its novel substrate
discs large homolog (DLG) 1 to promote
cellular migration. J Biol Chem jbc.
M111.294603
80. Kanzaki N, Ogita H, Komura H, Ozaki M,
Sakamoto Y, Majima T, Ijuin T, Takenawa T,
Takai Y (2008) Involvement of the nectinafadin complex in PDGF-induced cell survival. J Cell Sci 121:2008–2017
81. Sato T, Fujita N, Yamada A, Ooshio T,
Okamoto R, Irie K, Takai Y (2006) Regulation of the assembly and adhesion activity of
E-cadherin by nectin and afadin for the formation of adherens junctions in Madin-Darby
canine kidney cells. J Biol Chem
281:5288–5299
82. Ooshio T, Kobayashi R, Ikeda W, Miyata M,
Fukumoto Y, Matsuzawa N, Ogita H, Takai Y
(2010) Involvement of the interaction of afadin with ZO-1 in the formation of tight junctions in Madin-Darby canine kidney cells. J
Biol Chem 285:5003–5012
83. Severson EA, Lee WY, Capaldo CT, Nusrat A,
Parkos CA (2009) Junctional adhesion molecule A interacts with afadin and PDZ-GEF2
to activate Rap1A, regulate β1 integrin levels,
and enhance cell migration. Mol Biol Cell
20:1916–1925
84. Asada M, Irie K, Morimoto K, Yamada A,
Ikeda W, Takeuchi M, Takai Y (2003) ADIP,
a novel afadin- and α-actinin-binding protein
localized at cell-cell adherens junctions. J Biol
Chem 278:4103–4111
85. Mandai K, Nakanishi H, Satoh A, Obaishi H,
Wada M, Nishioka H, Itoh M, Mizoguchi A,
Aoki T, Fujimoto T, Matsuda Y, Tsukita S,
Takai Y (1997) Afadin: a novel actin
filament-binding protein with one PDZ
domain localized at cadherin-based cell-tocell adherens junction. J Cell Biol
139:517–528
273
86. Mandai K, Nakanishi H, Satoh A,
Takahashi K, Satoh K, Nishioka H,
Mizoguchi A, Takai Y (1999) Ponsin/
SH3P12: an l-afadin– and vinculin-binding
protein localized at cell–cell and cell–matrix
adherens
junctions.
J
Cell
Biol
144:1001–1018
87. Ooshio T, Irie K, Morimoto K, Fukuhara A,
Imai T, Takai Y (2004) Involvement of
LMO7 in the association of two cell-cell adhesion molecules, nectin and E-cadherin,
through afadin and alpha-actinin in epithelial
cells. J Biol Chem 279:31365–31373
88. Takai Y, Nakanishi H (2003) Nectin and afadin: novel organizers of intercellular junctions. J Cell Sci 116:17–27
89. Monteiro AC, Sumagin R, Rankin CR,
Leoni G, Mina MJ, Reiter DM, Stehle T,
Dermody TS, Schaefer SA, Hall RA,
Nusrat A, Parkos CA (2013) JAM-A associates with ZO-2, afadin, and PDZ-GEF1 to
activate Rap2c and regulate epithelial barrier
function. Mol Biol Cell 24:2849–2860
90. Fanning AS, Van Itallie CM, Anderson JM
(2012) Zonula occludens-1 and -2 regulate
apical cell structure and the zonula adherens
cytoskeleton in polarized epithelia. Mol Biol
Cell 23:577–590
91. Miyata M, Rikitake Y, Takahashi M,
Nagamatsu Y, Yamauchi Y, Ogita H,
Hirata K, Takai Y (2009) Regulation by afadin
of cyclical activation and inactivation of Rap1,
Rac1, and RhoA small G proteins at leading
edges of moving NIH3T3 cells. J Biol Chem
284:24595–24609
92. Choi W, Acharya BR, Peyret G, Fardin M-A,
Mège R-M, Ladoux B, Yap AS, Fanning AS,
Peifer M (2016) Remodeling the zonula
adherens in response to tension and the role
of afadin in this response. J Cell Biol
213:243–260
93. Ebnet K, Schulz CU, Zu Brickwedde M-KM,
Pendl GG, Vestweber D (2000) Junctional
adhesion molecule interacts with the PDZ
domain-containing proteins AF-6 and ZO-1.
J Biol Chem 275:27979–27988
94. Scott DW, Tolbert CE, Burridge K (2016)
Tension on JAM-A activates RhoA via
GEF-H1 and p115 RhoGEF. Mol Biol Cell
27:1420–1430
95. Priya R, Yap AS (2015) Active tension: the
role of cadherin adhesion and signaling in
generating junctional contractility. Curr Top
Dev Biol 112:65–102
96. Iden S, Misselwitz S, Peddibhotla SSD,
Tuncay H, Rehder D, Gerke V, Robenek H,
Suzuki A, Ebnet K (2012) aPKC
274
Elsa Bazellières and André Le Bivic
phosphorylates JAM-A at Ser285 to promote
cell contact maturation and tight junction formation. J Cell Biol 196:623–639
97. Albertson R (2004) Scribble protein domain
mapping reveals a multistep localization
mechanism and domains necessary for establishing cortical polarity. J Cell Sci
117:6061–6070
98. Zeitler J, Hsu CP, Dionne H, Bilder D (2004)
Domains controlling cell polarity and proliferation in the Drosophila tumor suppressor
Scribble. J Cell Biol 167:1137–1146
99. Audebert S, Navarro C, Nourry C, ChasserotGolaz S, Lécine P, Bellaiche Y, Dupont J-L,
Premont RT, Sempéré C, Strub J-M, Van
Dorsselaer A, Vitale N, Borg J-P (2004)
Mammalian Scribble forms a tight complex
with the betaPIX exchange factor. Curr Biol
14:987–995
100. Kallay LM, McNickle A, Brennwald PJ, Hubbard AL, Braiterman LT (2006) Scribble
associates with two polarity proteins, lgl2
and vangl2, via distinct molecular domains. J
Cell Biochem 99:647–664
101. Wang X, Dong B, Zhang K, Ji Z, Cheng C,
Zhao H, Sheng Y, Li X, Fan L, Xue W, Gao
W-Q, Zhu HH (2018) E-cadherin bridges
cell polarity and spindle orientation to ensure
prostate epithelial integrity and prevent carcinogenesis in vivo. PLoS Genet 14:e1007609
102. Osmani N, Vitale N, Borg J-P, Etienne-Manneville S (2006) Scrib controls Cdc42 localization and activity to promote cell
polarization during astrocyte migration.
Curr Biol 16:2395–2405
103. Lohia M, Qin Y, Macara IG (2012) The
Scribble polarity protein stabilizes E-cadherin/p120-catenin binding and blocks
retrieval of E-cadherin to the golgi. PLoS
One 7:e51130
104. le Duc Q, Shi Q, Blonk I, Sonnenberg A,
Wang N, Leckband D, de Rooij J (2010)
Vinculin potentiates E-cadherin mechanosensing and is recruited to actin-anchored sites
within adherens junctions in a myosin
II-dependent
manner.
J
Cell
Biol
189:1107–1115
105. Borghi N, Sorokina M, Shcherbakova OG,
Weis WI, Pruitt BL, Nelson WJ, Dunn AR
(2012) E-cadherin is under constitutive
actomyosin-generated tension that is
increased at cell–cell contacts upon externally
applied stretch. Proc Natl Acad Sci U S A
109:12568–12573
106. Bazellières E, Conte V, Elosegui-Artola A,
Serra-Picamal X, Bintanel-Morcillo M, RocaCusachs P, Muñoz JJ, Sales-Pardo M,
Guimerà R, Trepat X (2015) Control of cellcell forces and collective cell dynamics by the
intercellular adhesome. Nat Cell Biol
17:409–420
107. Wu SK, Lagendijk AK, Hogan BM, Gomez
GA, Yap AS (2015) Active contractility at
E-cadherin junctions and its implications for
cell extrusion in cancer. Cell Cycle
14:315–322
108. Engl W, Arasi B, Yap LL, Thiery JP, Viasnoff
V (2014) Actin dynamics modulate mechanosensitive immobilization of E-cadherin at
adherens
junctions.
Nat
Cell
Biol
16:584–591
109. Wu SK, Gomez GA, Michael M, Verma S,
Cox HL, Lefevre JG, Parton RG, Hamilton
NA, Neufeld Z, Yap AS (2014) Cortical
F-actin stabilization generates apical-lateral
patterns of junctional contractility that integrate cells into epithelia. Nat Cell Biol
16:167–178
110. Lugo-Martı́nez V-H, Petit CS, Fouquet S, Le
Beyec J, Chambaz J, Pinçon-Raymond M,
Cardot P, Thenet S (2009) Epidermal growth
factor receptor is involved in enterocyte anoikis through the dismantling of E-cadherinmediated junctions. Am J Physiol Gastrointest Liver Physiol 296:G235–G244
111. Choi J, Troyanovsky RB, Indra I, Mitchell BJ,
Troyanovsky SM (2019) Scribble, Erbin, and
Lano redundantly regulate epithelial polarity
and apical adhesion complex. J Cell Biol
218:2277–2293
112. Yoshihara K, Ikenouchi J, Izumi Y, Akashi M,
Tsukita S, Furuse M (2011) Phosphorylation
state regulates the localization of Scribble at
adherens junctions and its association with Ecadherin-catenin complexes. Exp Cell Res
317:413–422
113. Meng W, Mushika Y, Ichii T, Takeichi M
(2008) Anchorage of microtubule minus
ends to adherens junctions regulates epithelial
cell-cell contacts. Cell 135:948–959
114. Priya R, Yap AS, Gomez GA (2013)
E-cadherin supports steady-state Rho signaling at the epithelial zonula adherens. Differentiation 86:133–140
115. Truong Quang B-A, Mani M, Markova O,
Lecuit T, Lenne P-F (2013) Principles of
E-cadherin supramolecular organization
in vivo. Curr Biol 23:2197–2207
116. Hart KC, Tan J, Siemers KA, Sim JY, Pruitt
BL, Nelson WJ, Gloerich M (2017)
E-cadherin and LGN align epithelial cell divisions with tissue tension independently of cell
shape. Proc Natl Acad Sci U S A 114(29):
Mechanoregulation of PDZ Proteins
E5845–E5853. https://doi.org/10.1073/
pnas.1701703114
117. Gloerich M, Bianchini JM, Siemers KA,
Cohen DJ, Nelson WJ (2017) Cell division
orientation is coupled to cell–cell adhesion by
the E-cadherin/LGN complex. Nat Commun 8:1–11
118. del Rio A, Perez-Jimenez R, Liu R, RocaCusachs P, Fernandez JM, Sheetz MP
(2009) Stretching single talin rod molecules
activates
vinculin
binding.
Science
323:638–641
119. Mitrossilis D, Fouchard J, Pereira D, Postic F,
Richert A, Saint-Jean M, Asnacios A (2010)
Real-time single-cell response to stiffness.
PNAS 107:16518–16523
120. Choquet D, Felsenfeld DP, Sheetz MP
(1997) Extracellular matrix rigidity causes
strengthening of integrin–cytoskeleton linkages. Cell 88:39–48
275
121. Stehbens SJ, Paszek M, Pemble H,
Ettinger A, Gierke S, Wittmann T (2014)
CLASPs
link
focal-adhesion-associated
microtubule capture to localized exocytosis
and adhesion site turnover. Nat Cell Biol
16:561–573
122. Sackmann E, Smith A-S (2014) Physics of cell
adhesion: some lessons from cell-mimetic systems. Soft Matter 10:1644–1659
123. Riveline D, Zamir E, Balaban NQ, Schwarz
US, Ishizaki T, Narumiya S, Kam Z, Geiger B,
Bershadsky AD (2001) Focal contacts as
mechanosensors externally applied local
mechanical force induces growth of focal contacts by an mdia1-dependent and rockindependent mechanism. J Cell Biol
153:1175–1186
124. Kaverina I, Krylyshkina O, Beningo K,
Anderson K, Wang Y-L, Small JV (2002) Tensile stress stimulates microtubule outgrowth
in living cells. J Cell Sci 115:2283–2291
Chapter 16
Rational Design of PDZ Domain Inhibitors: Discovery of
Small Organic Compounds Targeting PDZ Domains
Laurent Hoffer, Philippe Roche, and Xavier Morelli
Abstract
PDZ domains, which belong to protein–protein interaction networks, are critical for regulating important
biological processes such as scaffolding, trafficking, and signaling cascades. Interfering with PDZ-mediated
interactions could affect these numerous biological processes. Thus, PDZ domains have emerged as
promising targets to decipher biological phenomena and potentially treat cancer and neurological diseases.
In this minireview, we focus on the discovery and design of small molecule inhibitors to modulate PDZ
domains. These compounds interfere with endogenous protein partners from the PDZ domain by binding
at the protein–protein interface. While peptides or peptidomimetic ligands were described to modulate
PDZ domains, the focus of this review is on small organic compounds.
Key words PDZ domain, PDZ inhibitors, Rational design, Screening, Molecular modeling
1
Introduction
Postsynaptic density protein 95/Drosophila disc large tumor suppressor/Zonula occludens 1 (PDZ) proteins are a family of proteins that contain at least one PDZ domain. The binding of protein
partners to PDZ domains mediates various processes, such as the
formation of protein networks, the immobilization of these proteins in the correct cellular compartment and the ability of promoting scaffolding, trafficking, and signaling events [1–4].
The PDZ domain is a common structural domain of approximately 90 amino acids found in signaling proteins of many organisms. The global structure is conserved and usually consists of 5–6
β-strands and 2 α-helixes. In general, PDZ domains have a shallow
binding site located between one β-strand and one α-helix that
recognizes the C-terminus (terminal carboxylate and last residues)
of their protein partners (Fig. 1).
C-terminal residues from a protein partner form an additional
antiparallel β-sheet by interacting with residues of the PDZ domain
through hydrogen bonds with backbone atoms. In addition, the
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021
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Fig. 1 3D crystallographic structure of the second PDZ domain of Syntenin (Synt-PDZ-2). (a) General fold of
Synt-PDZ-2. The protein is shown as cartoon representation and the carboxylate binding loop is highlighted.
(b) Structure of “Synt-PDZ-2/peptide” complex (PDB ID: 1OBY). The PDZ domain and peptide (C-terminus from
Syndecan-4: TNEFYA) are displayed as surface and sticks representation, respectively. The shallow hydrophobic subpocket, recognizing the terminal hydrophobic tail from the peptide partner, is highlighted. (c)
Detailed view of “Synt-PDZ-2/peptide” complex. The PDZ domain and its peptide partner are displayed as
cartoon and sticks representation, respectively. Direct hydrogen bonds between both entities are represented
as black dashed lines. Syntenin residues involved in hydrogen bonds with the peptide partner are colored in
pink. (d) Two-dimensional interaction diagram between PDZ-2 domain of Syntenin and TNEFYA peptide
partner. 3D structures were generated using Pymol (www.pymol.org) and MOE (https://www.chemcomp.com)
was used to create the 2D interaction diagram
C-terminal carboxylate group from the protein partner interacts
with the backbone of a conserved loop in the PDZ domain through
a canonically conserved hydrogen bond network [2].
More precisely, PDZ domains typically require a hydrophobic
residue at the C-terminal position of their protein partner, and this
residue fits into a small hydrophobic pocket near the carboxylate
binding loop. Typical hydrophobic residues are valine, leucine and
isoleucine. By convention, P0 refers to the C-terminal residue of
the peptide, and P-n refers to the nth amino acid of the peptide
starting from its C-terminal end. PDZ domains have been classified
according to their specificity for PDZ ligands. Consensus sequences
Rational Design of PDZ Domain Inhibitors
279
were built to define these groups [5]. Briefly, “class 1” PDZ
domains recognize C-terminal sequences with either a serine or
threonine at the 2 position. Similarly, “class 2” PDZ domains
bind to C-terminal sequences with a large hydrophobic or aromatic
residue at the 2 position. Finally, the consensus sequence recognized by “class 3” PDZ domains includes a negatively charged
residue at the 2 position. Other positions, such as 1 and 3,
have fewer constraints regarding the nature of the residue sidechain. Due to these small constraints, many peptides can bind
different PDZ domains [6], so achieving high specificity is expected
to be challenging.
It is also known that some PDZ domains are able to bind to
lipids. For instance, syntenin1 can interact with phosphatidylinositol phosphates with an affinity in the micromolar range [7].
2
PDZ Domains as Potential Drug Targets
PDZ domains, which are critical for regulating important
biological processes, have emerged as promising targets to treat
cancer and neurological diseases [1, 8–13]. Early inhibitors of
PDZ domains consisted of using short amino acid sequences representing the key C-terminal residues from endogenous partners.
Later, modified peptides, including thioketone and nonnatural
residues, were reported as biological tools to study PDZ domains
[14, 15]. Bivalent peptides were also shown to exhibit high affinity
for PDZ domains by simultaneously interacting with multiple PDZ
domains from the same protein [16]. In addition, TAT-derived
bivalent peptides, which contain cell permeability tags, were also
developed as efficient probes [16]. Despite their potential high
affinities, peptides may suffer from protein degradation by proteases and cell permeability issues. Thus, an appealing alternative
strategy is to develop small molecule inhibitors for oral administration that bypass these issues.
However, targeting PDZ domains may be very challenging
because this requires tackling the general problem of protein–protein interactions (PPIs). It has been shown that modulating PPIs
using small organic compounds is difficult due to the nature of the
interface [17]. In general, such interfaces are large and flat with
several small adjacent subpockets and are not expected to be easily
druggable [18, 19]. Several screening studies concluded, as
expected, that PDZ domains are mainly undruggable targets to
be modulated by small organic molecules and even fragments
[20, 21]. Indeed, the screening of fragment-like compounds is
considered a powerful tool to assess the druggability of a given
target [21]. These disappointing results are not truly surprising
from a structural point of view; the nature of the PDZ domain
binding pocket appears to be shallow with only a few putative
280
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Fujii et al
JACS (2003)
Marcotte et al
ProteinSci (2017)
Grandy et al
JBioChem (2009)
Shan et al
ChemBioDrugDes (2012)
Fujii et al, BioMedChemLet (2007)
Fujii et al, CancerRes (2007)
Lin et al
SciReports (2018)
Ma et al
JCAMD (2018)
Thorsen et al
PNAS (2010)
Bach et al
OrgBioChem (2010)
Choi et al
BioorgMedChem (2016)
Saupe et al
ChemMedChem (2011)
Joshi et al
AngewChem (2006)
Bouzidi et al
BioMedChemLet (2013)
Hori et al
FrontierPharm (2018)
Kim et al
MolecMed (2016)
Vogrig et al
ACSChemBio (2013)
Mayasundari et al
BioMedChemLet (2008)
Lee et al
AngewChem (2009)
Vargas et al
ChemMedChem (2014)
Cartier et al
PLOS (2015)
Bach et al
MedChemComm (2016)
Kegelman et al
PNAS (2017)
Fig. 2 2D structures and references of small organic inhibitors discussed in the minireview
interacting hotspots. Despite the fact that PDZ domains are poorly
suited for the development of small organic probes, different studies were reported in which inhibitor compounds were discovered.
The ultimate goals are to develop probes to decipher PDZ-related
biology and potential therapeutic drugs that target PDZ domains.
However, most compounds exhibited inhibition in the 10 micromolar range, despite intensive structure–activity relationship (SAR)
studies where close analogs are synthesized and tested. These compounds were discovered using either a screening-based strategy,
molecular modeling experiment or a combination of both
approaches. Currently reported PDZ inhibitors are quickly
reviewed below, and their 2D structures are depicted in Fig. 2.
Fujii et al. reported the first cell-permeable irreversible inhibitor targeting a PDZ domain (MAGI3-PDZ2) in 2003 [22]. The
designed compound was a potent inhibitor of the interaction
between PTEN and MAGI3, presenting an original covalent
mode of action. Docking studies using the DOCK tool [23] suggested that the indole-3-carbinol moiety could mimic the hydrophobic end of the partner while preorienting chemical groups
toward crucial residues from the MAGI3-PDZ2 domain. It should
be noted that this compound was covalently bound to the MAGI3PDZ2 domain through the histidine residue H372. Starting from
this covalent binder [22], Fujii et al. reported a reversible version of
their indole-containing inhibitor for the same PDZ domain
[24]. As previously described, DOCK software guided the rational
Rational Design of PDZ Domain Inhibitors
281
design of new compounds to mimic the four C-terminal residues of
the protein partner. This ligand was able to displace the reference
peptide probe in a concentration-dependent manner. However, the
affinity of the compound for MAGI3-PDZ was relatively weak, as a
competition was observed with concentrations in the 100 μM
range. In another study, the same compound was able to disrupt
the interaction between the Frizzed-7 Wnt receptor and the PDZ
domain of Dishevelled [25], leading to the downregulation of the
canonical Wnt signaling and suppression of tumor cell growth.
According to this study, the compound was among the first nonpeptide inhibitors to show therapeutic efficacy through the disruption of a PDZ PPI. Finally, this indole core was also used to target
the PDZ domain of NHERF1 [26]. Similar to the study from Fujii
et al., the DOCK tool was employed to suggest putative inhibitors
designed around the indole moiety [23]. In contrast to previous
work, an additional carboxylate group was incorporated at the end
of the flexible aliphatic sidechain of the compound to mimic the
aspartic residue from the reference partner.
In 2013, Vogrig et al. published small compounds able to
disrupt the PSD-95-PDZ1/5-HT2A receptor interaction leading
to an antihyperalgesic activity [27]. These inhibitors were discovered using a structure-based approach that combined molecular
modeling and NMR. A series of indole analogues were synthesized
on the basis of docking studies using AutoDock Vina software
[28]. Their ability to bind to the first PDZ domain (PDZ1) of
the PSD-95 protein was then assessed using NMR experiments.
The best compound exhibited a moderate IC50 value of 190 μM.
PDZ inhibitors, which were able to disrupt the PSD-95PDZ2/GluN2B PPI, were discovered using a rational “click chemistry” strategy [29]. The aim was to mimic the TAV/SAV tripeptide
PSD-95 ligand using triazole-containing compounds that were
easily synthesized from reactive azide and alkyne moieties. The
triazole heterocycle was chosen because it was previously reported
as a potential amide bioisostere while being more rigid and not
recognized by protein peptidases. One triazole-containing compound inhibited the PSD-95-PDZ2/GluN2B interaction with an
affinity similar to that measured for the SAV tripeptide. ITC experiments concluded that the compound had a low affinity for the
PSD-95-PDZ2 domain (Kd value in the 600 μM range). Finally,
molecular docking simulations using the Glide tool [30] suggested
that the triazole-containing compound interacts with the PDZ2
domain in a similar way as the TAV tripeptide.
In 2010, Thorsen et al. reported an organic compound
(FSC231) able to bind to the PDZ domain from PICK1 [9]. The
inhibitor was identified using a fluorescent polarization assay by
screening approximately 44,000 compounds. This inhibitor exhibited an affinity similar to that measured for peptide ligands
(C-terminal end from endogenous protein partners) in the 10 μM
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range. The AutoDock tool [31–33] was employed to predict the
binding mode of the compound within the PDZ binding site.
Molecular dynamics simulations were performed with the
AMBER package [34] to refine the predicted binding mode. During subsequent SAR studies around FSC231, Bach et al. managed
to slightly improve the Ki value by replacing chlorine atom in the
meta position with a trifluoromethyl group [35].
More potent chemical inhibitors targeting the PDZ domain
from PICK1 were recently published [36]. These inhibitors were
able to modulate the amyloid beta-mediated synaptic dysfunction
by interfering with the PICK1-PDZ/GluA2 PPI. Such potent
compounds in the submicromolar range are interesting for the
biological study of memory mechanisms and may be used as potential treatments for neurodegenerative disorders. An integrated
strategy involving high-throughput screening, structure-based
drug design, and biochemical and cellular assays was used to discover potent small molecule PICK1-PDZ inhibitors. This
structure-based strategy relied on determining the protein–ligand
structure using X-ray crystallography, followed by intensive SAR
studies to increase the potency of the series. The X-ray crystallography experiments were more difficult than expected because conventional methods and conditions failed to produce any cocrystal
structures. A new approach called the “lock and chop” method was
developed to tackle this issue [37]. Analysis of the X-ray crystal
structure of the complex (PDB ID: 6AR4) revealed that the chemical compound was located in the expected binding pocket and was
able to tightly interact with a phenylalanine sidechain that was not
targeted by the endogenous peptide. The most potent compound
from the series exhibited an approximately 200-fold better potency
than that from the C-terminal of the GluA2 partner. Selectivities
with respect to other reference PDZ domains were also measured
for this series of compounds. This remarkable integrated study
reported the highest affinity for small molecule inhibitors of the
PDZ domain known to date, with an IC50 value of 70 nM. The
X-ray crystal structure, deposited in the protein databank (PDB ID:
6AR4), corresponds to a compound from the series with an IC50
value of 600 nM. Unfortunately, these compounds were unable to
cross the blood brain barrier, preventing any potential use as drug
candidates. However, they can still be used as chemical probes in
biology studies due to their high potency.
NMR spectroscopy-based screening allowed the detection of
weakly binding inhibitors for the PDZ domain from AF6, which is
an essential component of cell junctions [38]. A dissociation constant (Kd) value of 100 μM, which is in the same range as last
residues from the endogenous EphB2 partner, was obtained with
one analog designed around the rhodanine core. More intensive
SAR studies around the same core were published several years later
[39]. The design of new compounds was guided by molecular
Rational Design of PDZ Domain Inhibitors
283
modeling using the 3D structure of the PDZ domain of interest.
The new derivatives were again evaluated using an NMR-based
approach. The best compound, a mixture of diastereoisomers,
exhibited a 5 μM affinity for AF6-PDZ. Molecular docking using
the MOE package (http://www.chemcomp.com) was performed
to identify the most likely active stereoisomer from the diastereoisomer mixture.
Dishevelled (Dvl) is an essential protein in the Wnt signaling
pathway that relies on its PDZ domain for transduction of downstream signals [11]. Interestingly, a known sulindac drug was
shown to inhibit the canonical Wnt signaling pathway by binding
to the PDZ domain of Dvl [40]. NMR experiments enabled the
determination of the “sulindac / PDZ domain” complex and concluded that sulindac is located within the peptide binding pocket of
the PDZ domain. Finally, a Ki value of 10 μM was measured for
sulindac in a competitive assay with a reference peptide. Additional
chemical compounds, which bind to the Dvl-PDZ domain in the
low micromolar range affinity, were discovered using a protocol
involving both molecular modeling and NMR spectroscopy
[11]. In silico experiments employed a structure-based pharmacophore search using the Unity module from Sybyl [41] to identify
small organic compounds that could mimic the binding mode of
the Dapper protein partner within the PDZ domain. Then, the
FlexX docking tool [42] was used to confirm the ability of selected
compounds to act as potential PDZ domain binders. NMR spectroscopy was used as an experimental method to validate (or not)
the selected compounds. A benzoic acid molecule, which displayed
the most significant chemical shift perturbations, exhibited a 10 μM
Kd value. Finally, in vivo studies confirmed its ability to reduce the
growth rate of prostate cancer cell lines. A similar strategy was used
in another study to target the PSD95-PDZ domain [43]. The
binding of the best identified fragment (quinoline-2,7-dicarboxylic
acid) was confirmed using NMR experiments.
Another study focused on the Dvl protein identified small
molecules that perturb the Dvl-PDZ/CXXC5 PPI [44]. Inhibition
of this interaction may have potential interest in bone anabolic
osteoporosis therapy by enhancing osteoblast differentiation
[44]. More than 50 analogs were synthesized to explore the chemical space around the hit while also trying to increase the microsomal
stability and optimize the physicochemical properties. Binding
modes of representative compounds from the series were predicted
using molecular docking with DOCKER from the Discovery Studio package [45]. The best compound, which exhibited a Dvl-PDZ
binding affinity of 8 μM, successfully rescued bone loss in an
ovariectomized mouse model. These authors also reported additional studies that focused on the same Dvl protein and employed
various computational approaches to identify other PDZ inhibitors
[46, 47]. For example, in the work reported by Ma et al. [47], X-ray
284
Laurent Hoffer et al.
crystal structures of Dvl-PDZ bound to an organic compound and
snapshots from molecular dynamics simulations of the Dvl-PDZ/
peptide complex guided the creation of pharmacophore models.
These models combined with a virtual screening of a large chemical
library allowed the identification of compounds that could mimic
the binding mode of reference molecules. Fluorescence spectroscopy and NMR experiments confirmed the binding of several compounds at the Dvl-PDZ–CXXC5 interface, and the best compound
had a Kd value of 22 μM.
Using a combination of NMR, quantitative structure–activity
relationship (QSAR) and structure-based pharmacophore filtering,
Shan et al. identified and optimized inhibitors for the Dvl-PDZ
domain [48]. This series of compounds essentially consists of
merging a benzoic acid moiety with two protein residues. The
best compound from the series exhibited a Ki value of 1.5 μM in
the fluorescence polarization assay. Potential binding modes were
predicted using the Glide docking tool [30] and matched those
from endogenous partners.
Hori et al. reported new inhibitors for the Dvl-PDZ domain
using the “NMR/Docking Performance Index” (NMR-DPI) protocol, which relies on both NMR and molecular docking experiments [49]. Several reference inhibitors were investigated with
GOLD as the docking engine [50, 51] to select the best scoring
scheme using different scoring functions (ChemScore, GoldScore,
and ChemPLP) and with and without consensus scoring. The best
scoring protocol was then employed for virtual screening with a
focused library (approximately 5 K compounds). In total, 13 compounds were selected to be experimentally tested, and several of
them showed partial proliferation inhibition activity against a triplenegative breast cancer cell line.
Saupe et al. reported in 2011 a study about the Shank3-PDZ
domain [10]. The ChemBioNet library was first screened using a
fluorescence polarization assay, and one natural product-like scaffold (cyclopentyl-tetrahydroquinoline-carboxylates) emerged as a
PDZ inhibitor. SAR studies around this core were performed to
optimize its potency. The best compound analog exhibited a Ki
value in the 10 μM range, and the binding of the compound within
the PDZ domain was confirmed by NMR experiments. Then, X-ray
crystallography studies were used to determine the structure of
Shank3-PDZ/inhibitor complex (PDB ID: 3O5N) and to confirm
its ability to mimic the C-terminal end of the protein partner.
Kegelman et al. disclosed one chemical inhibitor, which targets
the first PDZ domain of syntenin (Synt-PDZ1), using an integrated
strategy involving an NMR-based screening of fragment-like compounds, SAR studies, and molecular modeling [13]. More precisely, approximately 5 K fragments were initially evaluated using
an NMR-based screening. Two nonoverlapping fragments were
identified, and a structure-based linking strategy was employed to
Rational Design of PDZ Domain Inhibitors
285
merge them. Ultimately, a combination of molecular docking studies using GOLD software [50, 51] and SAR studies produced a
fused compound that exhibited a dissociation constant (Kd) of
21 μM for Synt-PDZ1. From a biological point of view, glioblastoma multiform (GBM) is one of the most aggressive cancers and is
associated with short survival times and poor response to radiotherapy because of its invasive properties. It has been shown that
syntenin is overexpressed in this kind of cancer. Both genetic and
pharmacological strategies to modulate syntenin reduced invasion
gains in GBM cells following radiation. Finally, intraperitoneal
administration of the developed inhibitor improved the survival of
brain tumor-bearing mice.
Finally, we also reported an integrated study by combining
proteomic and genetic techniques with structural biochemistry
and molecular modeling, providing a detailed discovery of small
compounds targeting the PDZ domain from GRASP55 [52]. The
impacts on germ cell Golgi remodeling and spermatogenesis after
administration of the compound were also studied. First, X-ray
crystal structures of GRASP55 in complex with JAM-C or JAM-B
were obtained and revealed that GRASP55 underwent conformational changes with respect to its free conformation, with the latter
being more open. An in silico protocol involving high-throughput
docking and pharmacophore filtering was performed to identify
potential GRASP55-PDZ inhibitors. A library of 200,000 compounds dedicated to PPIs was used, and these compounds were
docked into the binding site of the closed GRASP55-PDZ conformation using the Surflex docking tool [53], thereby producing
millions of poses. Pharmacophore filtering, using Unity package
from Sybyl [41], was then used to extract compounds that could
mimic the canonical binding mode of the JAM peptides. Approximately 50 molecules were purchased and tested experimentally
using homogeneous time-resolved fluorescence (HTRF), leading
to the identification of a chemical compound that inhibited the
GRASP55-PDZ–JAM interaction with an IC50 of 8 μM. Unfortunately, despite intensive efforts, an X-ray crystal structure for this
compound in complex with GRASP55-PDZ was not obtained. In
the end, the biological relevance of the GRASP55-PDZ–JAM-C
interaction in spermatogenesis was validated using both genetic
ablation of the encoding GRASP55 gene and disruption of this
PPI using a small organic compound. Treatment of mice with the
inhibitor induced premature release of spermatids and germ cell
loss; thus, this inhibitor has potential to be used as male
contraception.
286
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Laurent Hoffer et al.
Conclusion
There is growing interest in the development of compounds able to
modulate PPIs, as they control a large number of physiological
events and are involved in many diseases [54, 55]. However, PPIs
are considered challenging targets for the development of chemical
probes or drugs. PDZ domains belong to PPI networks, and therefore are essentially considered poor druggable targets. This is confirmed by the screening of large compound libraries in which no
high affinity hits were yet identified [20]. Despite this classification
as being poor druggable targets, dozens of studies have reported
small organic compounds able to disrupt PPIs between PDZ
domains and their endogenous protein partners. Various strategies
involving a combination of experimental screening, biophysical
methods, molecular modeling, and organic chemistry were
employed to tackle the development of PDZ inhibitors. However,
most nonpeptide compounds reported thus far exhibited moderate
affinity in the 10 μM range. Promisingly, several integrative studies
recently reported submicromolar compounds, enabling future
opportunities for the use of PDZ inhibitors as important tools to
decipher biological processes and as potential therapeutics to treat
some cancers and neurological diseases.
References
1. Wang NX, Lee HJ, Zheng JJ (2008) Therapeutic use of PDZ protein-protein interaction
antagonism. Drug News Perspect 21
(3):137–141
2. Chi CN, Bach A, Strømgaard K, Gianni S,
Jemth P (2012) Ligand binding by PDZ
domains. Biofactors 38(5):338–348. https://
doi.org/10.1002/biof.1031
3. Dev KK (2004) Making protein interactions
druggable: targeting PDZ domains. Nat Rev
Drug Discov 3(12):1047–1056. https://doi.
org/10.1038/nrd1578
4. Grillo-Bosch D, Choquet D, Sainlos M (2013)
Inhibition of PDZ domain-mediated interactions. Drug Discov Today Technol 10(4):
e531–e540.
https://doi.org/10.1016/j.
ddtec.2012.10.003
5. Songyang Z, Fanning AS, Fu C, Xu J, Marfatia
SM, Chishti AH, Crompton A, Chan AC,
Anderson JM, Cantley LC (1997) Recognition
of unique carboxyl-terminal motifs by distinct
PDZ domains. Science 275(5296):73–77.
https://doi.org/10.1126/science.275.
5296.73
6. Wiedemann U, Boisguerin P, Leben R,
Leitner D, Krause G, Moelling K, Volkmer-
Engert R, Oschkinat H (2004) Quantification
of PDZ domain specificity, prediction of ligand
affinity and rational design of super-binding
peptides. J Mol Biol 343(3):703–718.
https://doi.org/10.1016/j.jmb.2004.08.064
7. Wawrzyniak
AM,
Vermeiren
E,
Zimmermann P, Ivarsson Y (2012) Extensions
of PSD-95/discs large/ZO-1 (PDZ) domains
influence lipid binding and membrane targeting of syntenin-1. FEBS Lett 586
(10):1445–1451. https://doi.org/10.1016/j.
febslet.2012.04.024
8. Aarts M, Liu Y, Liu L, Besshoh S, Arundine M,
Gurd JW, Wang YT, Salter MW, Tymianski M
(2002) Treatment of ischemic brain damage by
perturbing NMDA receptor- PSD-95 protein
interactions. Science 298(5594):846–850.
https://doi.org/10.1126/science.1072873
9. Thorsen TS, Madsen KL, Rebola N, Rathje M,
Anggono V, Bach A, Moreira IS, StuhrHansen N, Dyhring T, Peters D, Beuming T,
Huganir R, Weinstein H, Mulle C,
Strømgaard K, Rønn LC, Gether U (2010)
Identification of a small-molecule inhibitor of
the PICK1 PDZ domain that inhibits hippocampal LTP and LTD. Proc Natl Acad Sci U S
Rational Design of PDZ Domain Inhibitors
A
107(1):413–418.
https://doi.org/10.
1073/pnas.0902225107
10. Saupe J, Roske Y, Schillinger C, Kamdem N,
Radetzki S, Diehl A, Oschkinat H, Krause G,
Heinemann U, Rademann J (2011) Discovery,
structure-activity relationship studies, and crystal structure of nonpeptide inhibitors bound to
the Shank3 PDZ domain. ChemMedChem 6
(8):1411–1422. https://doi.org/10.1002/
cmdc.201100094
11. Grandy D, Shan J, Zhang X, Rao S, Akunuru S,
Li H, Zhang Y, Alpatov I, Zhang XA, Lang RA,
Shi DL, Zheng JJ (2009) Discovery and characterization of a small molecule inhibitor of the
PDZ domain of dishevelled. J Biol Chem 284
(24):16256–16263.
https://doi.org/10.
1074/jbc.M109.009647
12. Patra CR, Rupasinghe CN, Dutta SK,
Bhattacharya S, Wang E, Spaller MR, Mukhopadhyay D (2012) Chemically modified peptides targeting the PDZ domain of GIPC as a
therapeutic approach for cancer. ACS Chem
Biol
7(4):770–779.
https://doi.org/10.
1021/cb200536r
13. Kegelman TP, Wu B, Das SK, Talukdar S,
Beckta JM, Hu B, Emdad L, Valerie K,
Sarkar D, Furnari FB, Cavenee WK, Wei J,
Purves A, De SK, Pellecchia M, Fisher PB
(2017) Inhibition of radiation-induced glioblastoma invasion by genetic and pharmacological targeting of MDA-9/Syntenin. Proc
Natl Acad Sci U S A 114(2):370–375.
https://doi.org/10.1073/pnas.1616100114
14. Bach A, Chi CN, Olsen TB, Pedersen SW,
Røder MU, Pang GF, Clausen RP, Jemth P,
Strømgaard K (2008) Modified peptides as
potent inhibitors of the postsynaptic density95/N-methyl-D-aspartate receptor interaction. J Med Chem 51(20):6450–6459.
https://doi.org/10.1021/jm800836w
15. Bach A, Eildal JN, Stuhr-Hansen N,
Deeskamp R, Gottschalk M, Pedersen SW,
Kristensen AS, Strømgaard K (2011) Cellpermeable and plasma-stable peptidomimetic
inhibitors of the postsynaptic density-95/Nmethyl-D-aspartate receptor interaction. J
Med Chem 54(5):1333–1346. https://doi.
org/10.1021/jm1013924
16. Bach A, Clausen BH, Møller M, Vestergaard B,
Chi CN, Round A, Sørensen PL, Nissen KB,
Kastrup JS, Gajhede M, Jemth P, Kristensen
AS, Lundström P, Lambertsen KL, Strømgaard
K (2012) A high-affinity, dimeric inhibitor of
PSD-95 bivalently interacts with PDZ1-2 and
protects against ischemic brain damage. Proc
Natl Acad Sci U S A 109(9):3317–3322.
https://doi.org/10.1073/pnas.1113761109
287
17. Wells JA, McClendon CL (2007) Reaching for
high-hanging fruit in drug discovery at
protein-protein interfaces. Nature 450
(7172):1001–1009.
https://doi.org/10.
1038/nature06526
18. Morelli X, Bourgeas R, Roche P (2011) Chemical and structural lessons from recent successes
in protein-protein interaction inhibition
(2P2I). Curr Opin Chem Biol 15
(4):475–481.
https://doi.org/10.1016/j.
cbpa.2011.1005.1024
19. Bourgeas R, Basse MJ, Morelli X, Roche P
(2010) Atomic analysis of protein-protein
interfaces with known inhibitors: the 2P2I
database. PLoS One 5(3):e9598. https://doi.
org/10.1371/journal.pone.0009598
20. Chen X, Longgood JC, Michnoff C, Wei S,
Frantz DE, Bezprozvanny L (2007) Highthroughput screen for small molecule inhibitors of Mint1-PDZ domains. Assay Drug Dev
Technol 5(6):769–783. https://doi.org/10.
1089/adt.2007.092
21. Hajduk P, Huth J, Fesik S (2005) Druggability
indices for protein targets derived from
NMR-based screening data. J Med Chem 48
(7):2518–2525. https://doi.org/10.1021/
jm049131r
22. Fujii N, Haresco JJ, Novak KA, Stokoe D,
Kuntz ID, Guy RK (2003) A selective irreversible inhibitor targeting a PDZ protein interaction domain. J Am Chem Soc 125
(40):12074–12075.
https://doi.org/10.
1021/ja035540l
23. Ewing TJ, Makino S, Skillman GA, Kuntz ID
(2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule
databases. J Comput Aided Mol Des 15
(5):411–428
24. Fujii N, Haresco JJ, Novak KA, Gage RM,
Pedemonte N, Stokoe D, Kuntz ID, Guy RK
(2007) Rational design of a nonpeptide general
chemical scaffold for reversible inhibition of
PDZ domain interactions. Bioorg Med Chem
Lett 17(2):549–552. https://doi.org/10.
1016/j.bmcl.2006.10.006
25. Fujii N, You L, Xu Z, Uematsu K, Shan J,
He B, Mikami I, Edmondson LR, Neale G,
Zheng J, Guy RK, Jablons DM (2007) An
antagonist of dishevelled protein-protein interaction suppresses beta-catenin-dependent
tumor cell growth. Cancer Res 67
(2):573–579.
https://doi.org/10.1158/
0008-5472.CAN-06-2726
26. Mayasundari A, Ferreira AM, He L,
Mahindroo N, Bashford D, Fujii N (2008)
Rational design of the first small-molecule
antagonists of NHERF1/EBP50 PDZ
domains. Bioorg Med Chem Lett 18
288
Laurent Hoffer et al.
(3):942–945.
https://doi.org/10.1016/j.
bmcl.2007.12.038
27. Vogrig A, Dorr L, Bouzidi N, Boucherle B,
Wattiez AS, Cassier E, Vallon G, Ripoche I,
Abrunhosa-Thomas I, Marin P, Nauton L,
Thery V, Courteix C, Lian LY, Ducki S
(2013) Structure-based design of PDZ ligands
as inhibitors of 5-HT(2A) receptor/PSD-95
PDZ1 domain interaction possessing antihyperalgesic activity. ACS Chem Biol 8
(10):2209–2216. https://doi.org/10.1021/
cb400308u
28. Trott O, Olson AJ (2010) AutoDock Vina:
improving the speed and accuracy of docking
with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31
(2):455–461. https://doi.org/10.1002/jcc.
21334
29. Bach A, Pedersen TB, Strømgaard K (2016)
Design and synthesis of triazole-based peptidomimetics of a PSD-95 PDZ domain inhibitor.
MedChemComm 7(3):531–536. https://doi.
org/10.1039/C5MD00445D
30. Friesner RA, Banks JL, Murphy RB, Halgren
TA, Klicic JJ, Mainz DT, Repasky MP, Knoll
EH, Shelley M, Perry JK, Shaw DE, Francis P,
Shenkin PS (2004) Glide: a new approach for
rapid, accurate docking and scoring. 1. Method
and assessment of docking accuracy. J Med
Chem 47(7):1739–1749. https://doi.org/10.
1021/jm0306430
31. Morris GM, Goodsell DS, Halliday RS,
Huey R, Hart WE, Belew RK, Olson AJ
(1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding
free energy function. J Comput Chem 19
(14):1639–1662
32. Goodsell DS, Morris GM, Olson AJ (1996)
Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 9(1):1–5
33. Morris GM, Huey R, Lindstrom W, Sanner
MF, Belew RK, Goodsell DS, Olson AJ
(2009) AutoDock4 and AutoDockTools4:
automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791.
https://doi.org/10.1002/jcc.21256
34. Case DA, Cheatham TE, Darden T, Gohlke H,
Luo R, Merz KM, Onufriev A, Simmerling C,
Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem
26(16):1668–1688.
https://doi.org/10.
1002/jcc.20290
35. Bach A, Stuhr-Hansen N, Thorsen TS, Bork N,
Moreira IS, Frydenvang K, Padrah S, Christensen SB, Madsen KL, Weinstein H, Gether U,
Strømgaard K (2010) Structure-activity relationships of a small-molecule inhibitor of the
PDZ domain of PICK1. Org Biomol Chem 8
(19):4281–4288. https://doi.org/10.1039/
c0ob00025f
36. Lin EYS, Silvian LF, Marcotte DJ, Banos CC,
Jow F, Chan TR, Arduini RM, Qian F, Baker
DP, Bergeron C, Hession CA, Huganir RL,
Borenstein CF, Enyedy I, Zou J, Rohde E,
Wittmann M, Kumaravel G, Rhodes KJ, Scannevin RH, Dunah AW, Guckian KM (2018)
Potent PDZ-domain PICK1 inhibitors that
modulate amyloid beta-mediated synaptic dysfunction. Sci Rep 8(1):13438. https://doi.
org/10.1038/s41598-018-31680-3
37. Marcotte DJ, Hus JC, Banos CC, Wildes C,
Arduini R, Bergeron C, Hession CA, Baker DP,
Lin E, Guckian KM, Dunah AW, Silvian LF
(2018) Lock and chop: a novel method for
the generation of a PICK1 PDZ domain and
piperidine-based inhibitor co-crystal structure.
Protein Sci 27(3):672–680. https://doi.org/
10.1002/pro.3361
38. Joshi M, Vargas C, Boisguerin P, Diehl A,
Krause G, Schmieder P, Moelling K, Hagen V,
Schade M, Oschkinat H (2006) Discovery of
low-molecular-weight ligands for the AF6
PDZ domain. Angew Chem Int Ed Engl 45
(23):3790–3795. https://doi.org/10.1002/
anie.200503965
39. Vargas C, Radziwill G, Krause G, Diehl A,
Keller S, Kamdem N, Czekelius C,
Kreuchwig A, Schmieder P, Doyle D,
Moelling K, Hagen V, Schade M, Oschkinat
H (2014) Small-molecule inhibitors of AF6
PDZ-mediated protein-protein interactions.
ChemMedChem 9(7):1458–1462. https://
doi.org/10.1002/cmdc.201300553
40. Lee HJ, Wang NX, Shi DL, Zheng JJ (2009)
Sulindac inhibits canonical Wnt signaling by
blocking the PDZ domain of the protein
Dishevelled. Angew Chem Int Ed Engl 48
(35):6448–6452. https://doi.org/10.1002/
anie.200902981
41. Tripos Sybyl. St. Louis, MO
42. Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction
algorithm for protein-ligand docking. Proteins
37(2):228–241. https://doi.org/10.1002/(
SICI)1097-0134(19991101)37:2<228::AIDPROT8>3.0.CO;2-8
43. Bouzidi N, Deokar H, Vogrig A, Boucherle B,
Ripoche I, Abrunhosa-Thomas I, Dorr L, Wattiez AS, Lian LY, Marin P, Courteix C, Ducki S
(2013) Identification of PDZ ligands by
docking-based virtual screening for the development of novel analgesic agents. Bioorg Med
Chem Lett 23(9):2624–2627. https://doi.
org/10.1016/j.bmcl.2013.02.100
44. Kim HY, Choi S, Yoon JH, Lim HJ, Lee H,
Choi J, Ro EJ, Heo JN, Lee W, No KT, Choi
Rational Design of PDZ Domain Inhibitors
KY (2016) Small molecule inhibitors of the
Dishevelled-CXXC5 interaction are new drug
candidates for bone anabolic osteoporosis therapy. EMBO Mol Med 8(4):375–387. https://
doi.org/10.15252/emmm.201505714
45. AccelrysSoftware Discovery Studio. San Diego,
CA
46. Choi J, Ma S, Kim HY, Yun JH, Heo JN,
Lee W, Choi KY, No KT (2016) Identification
of small-molecule compounds targeting the
dishevelled PDZ domain by virtual screening
and binding studies. Bioorg Med Chem 24
(15):3259–3266. https://doi.org/10.1016/j.
bmc.2016.03.026
47. Ma S, Choi J, Jin X, Kim HY, Yun JH, Lee W,
Choi KY, No KT (2018) Discovery of a smallmolecule inhibitor of Dvl-CXXC5 interaction
by computational approaches. J Comput Aided
Mol Des 32(5):643–655. https://doi.org/10.
1007/s10822-018-0118-x
48. Shan J, Zhang X, Bao J, Cassell R, Zheng JJ
(2012) Synthesis of potent dishevelled PDZ
domain inhibitors guided by virtual screening
and NMR studies. Chem Biol Drug Des 79
(4):376–383.
https://doi.org/10.1111/j.
1747-0285.2011.01295.x
49. Hori K, Ajioka K, Goda N, Shindo A,
Takagishi M, Tenno T, Hiroaki H (2018) Discovery of potent disheveled/Dvl inhibitors
using virtual screening optimized with
NMR-based docking performance index.
Front Pharmacol 9:983. https://doi.org/10.
3389/fphar.2018.00983
50. Jones G, Willett P, Glen R, Leach A, Taylor R
(1997) Development and validation of a
genetic algorithm for flexible docking. J Mol
289
Biol 267(3):727–748. https://doi.org/10.
1006/jmbi.1996.0897
51. Verdonk ML, Cole JC, Hartshorn MJ, Murray
CW, Taylor RD (2003) Improved proteinligand docking using GOLD. Proteins 52
(4):609–623. https://doi.org/10.1002/prot.
10465
52. Cartier-Michaud A, Bailly AL, Betzi S, Shi X,
Lissitzky JC, Zarubica A, Sergé A, Roche P,
Lugari A, Hamon V, Bardin F, Derviaux C,
Lembo F, Audebert S, Marchetto S,
Durand B, Borg JP, Shi N, Morelli X,
Aurrand-Lions M (2017) Genetic, structural,
and chemical insights into the dual function of
GRASP55 in germ cell Golgi remodeling and
JAM-C polarized localization during spermatogenesis. PLoS Genet 13(6):e1006803.
https://doi.org/10.1371/journal.pgen.
1006803
53. Jain AN (2003) Surflex: fully automatic flexible
molecular docking using a molecular similaritybased search engine. J Med Chem 46
(4):499–511. https://doi.org/10.1021/jm.
020406h
54. Petta I, Lievens S, Libert C, Tavernier J, De
Bosscher K (2016) Modulation of proteinprotein interactions for the development of
novel therapeutics. Mol Ther 24(4):707–718.
https://doi.org/10.1038/mt.2015.214
55. Ivanov AA, Revennaugh B, Rusnak L,
Gonzalez-Pecchi V, Mo X, Johns MA, Du Y,
Cooper LAD, Moreno CS, Khuri FR, Fu H
(2018) The OncoPPi portal: an integrative
resource to explore and prioritize proteinprotein interactions for cancer target discovery.
Bioinformatics 34(7):1183–1191. https://doi.
org/10.1093/bioinformatics/btx743
INDEX
A
Actomyosin................................. 261, 263, 264, 266–268
Adherens junctions (AJ) ............................................... 222
Amide-to ester mutation ....................197, 206, 210–211
AVLX-144 ................................................... 158, 159, 163
B
BIAcore......................................................................75–86
Biomolecular fragment complementation assay.......... 187
Biophysical techniques .................................................... 90
C
Calcium/calmodulin-dependent serine protein
kinase (CASK) ........................... 34, 138–140, 143
Computational approach ....................................... 66, 237
E
Electropherogram superimposition ............................... 67
Epithelial cell polarity ................................................... 224
Equilibrium ......................... 62, 78, 84, 95, 99, 149–154
Expressed protein ligation (EPL)....................... 194–197,
199, 200, 211, 212
F
Fluorescence ............................................... 66, 67, 73, 99,
107, 116, 137–147, 150, 151, 153, 161, 163,
169, 170, 180, 181, 184–186
Fluorescence anisotropy ............................................... 138
Fluorescence polarization (FP) ............................ 73, 161,
163, 169, 170, 180, 181, 184–186, 188
Folding ................................................... 3, 90, 91, 97–99,
104, 105, 114–119, 127, 130, 149–155
Forces.....................................................95, 238, 246, 247
G
GAL4 ............................................................................. 1, 2
H
Holdup assay .................................................... 61–72, 180
I
Identity ..............................................90, 92–94, 138, 227
Implicit solvent............................................ 239, 240, 245
Inhibition......................................................157, 224–226
Isothermal titration calorimetry (ITC).......................127,
129, 130, 132, 170, 175, 180, 181, 186, 187, 190
K
Kinetics ....................................................... 77, 78, 81–84,
86, 115, 149, 150, 152–155, 180, 196
L
Ligand binding.................... 41, 117, 127, 137–147, 242
Lipid interactions ......................................................75–86
Liposomes....................................... 76–78, 80, 82, 83, 86
L1 sensor chips......................................78, 82, 83, 85, 86
M
MC simulations .................................................... 242, 243
Molecular mechanics............................................ 238–240
Molecular modeling............................280, 281, 283–286
N
NanoBiT ..............................................180, 183, 187–189
Native chemical ligation (NCL)................. 194, 212, 213
Next-generation sequencing (NGS) ...................... 42, 43,
46, 51, 56, 58
P
Pathogenesis ...................... 221, 223, 225–227, 230, 232
PDZ-Binding Motifs (PBM).................................. 3, 5, 7,
11, 12, 14, 19, 24, 41, 61–73, 75, 86, 89, 99, 115,
117, 126, 129, 132, 137, 139–142, 145,
179–190, 217–232
PDZ/PBM interaction ............................... 137, 138, 145
Peptide interactions ..........................................75, 81, 82,
84, 85, 125–132
Phage display ........................................... 41–58, 126, 130
Phosphorylation ..............................................42, 48, 126,
179–190, 197, 206, 224, 239
Protein complexes .............................................17, 18, 21,
22, 24, 26, 30–31, 34, 37, 76, 127, 129, 223, 258
Protein design ...................................................... 237, 250
Protein engineering ...................................................... 116
Protein modifications.................................................... 193
Protein-protein binding................................................ 218
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1, © Springer Science+Business Media, LLC, part of Springer Nature 2021
291
PDZ MEDIATED INTERACTIONS: METHODS AND PROTOCOLS
292 Index
Protein-protein interaction (PPIs) ..................... 1, 61, 89,
127, 137, 157, 169, 170, 179, 187–189, 193,
217, 258, 279
Protein sample......................................................... 93, 95,
106–109, 112, 130, 131, 146, 204–206
Protein stability ............................................................. 146
Proteomics.....................17, 32, 38, 41–58, 93, 126, 285
Proteus program .................................................. 243, 247
PSD-95/Discs Large/ZO-1 (PDZ).........................1–14,
17–39, 41–58, 61–72, 75–86, 89–121, 125–132,
137–147, 149–176, 179–190, 193–215,
217–232, 237–253
domains ..................................... 3, 18, 41, 61, 75, 89,
126, 137, 143, 149, 152, 179, 194, 217, 230
inhibitors ................................................................. 159
lipids..................................................................... 75–86
proteins ...........................................19, 34, 76, 84, 85,
126, 137, 141, 143, 146, 150, 197, 219, 222,
224, 226, 231, 237, 250
Purification .....................................4, 8, 9, 17–39, 42–45,
47–49, 51, 52, 59, 62, 106, 109, 138–140, 146,
162, 170, 175, 181, 183, 198–206, 213
Purity ....................................................24, 37, 51, 52, 76,
90–92, 99, 103, 105–110, 139, 165, 166, 194,
204, 213
Q
Quality control ........................................ 25, 29, 118, 120
Quantification ............................................ 20, 46, 47, 58,
61, 91, 92, 95, 107, 108, 140, 207
R
Rational design..................................................... 277–286
Replication..................................221, 226, 227, 230, 232
S
SA sensor chip ............................................. 77, 79, 84, 85
Screening .................................................... 2–4, 7, 10, 11,
13, 14, 159, 163, 279–282, 284, 286
Scribble ............................................... 125–132, 138–140,
145, 223–226, 231, 261, 267–269
Scribble module .......................................... 125, 127, 128
Sensor chip (SA)................................................. 77, 81, 82
SH3-containing guanine nucleotide exchange
factor (SGEF) .................................. 128, 139, 145
Solid-phase peptide synthesis ............................. 159, 163,
165, 166, 193, 200, 206
Specificity ........................................... 69, 70, 75, 90, 126,
127, 137, 138, 196, 197, 219, 238, 244, 258,
278, 279
Stability ...................................... 86, 90, 97–99, 104, 105,
114–119, 146, 151, 152, 162, 171, 173, 188,
223, 237–239, 241, 262, 269, 283
Stroke...................................................158, 159, 163, 172
Structural biology ......................................................... 282
Structure ..................................................... 90, 91, 97, 99,
114, 116, 118, 126, 127, 130, 132, 149, 150,
217, 237, 241, 242, 244, 247, 248, 258, 277,
278, 280, 282–285
T
Tight junction (TJs)............................217, 222, 259–261
Two-hybrid array ............................................. 11–13, 126
V
Virus...........................140, 221–223, 225–227, 230, 232
X
X-ray crystallography .................127, 129, 130, 282, 284
Y
Yeast strains ............................................. 2, 4, 6, 9, 11, 14
Yeast two-hybrid (Y2H) ........................... 1–14, 126, 230