Modular Design of Coiled Coils to Target bZIP
Transcription Factors
by
Jenifer Kaplan
B.S. Molecular and Cellular Biology
B.A. Mathematics
Johns Hopkins University, Baltimore, MD, 2008
SUBMITTED TO THE DEPARTMENT OF BIOLOGY IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
FEBRUARY 2014
 Massachusetts Institute of Technology.
All rights reserved.
Signature of Author: _________________________________________________
Department of Biology
February 3, 2014
Certified by:______________________________________________________________
Amy Keating
Associate Professor of Biology
Thesis Supervisor
Accepted by:_____________________________________________________________
Stephen Bell
Professor of Biology
Co-Chair, Biology Graduate Committee
1
2
Modular Design of Coiled Coils to Target bZIP Transcription Factors
by
Jenifer Kaplan
Submitted to the Department of Biology
On February 3, 2014 in partial fulfillment of the requirements for the degree of Doctor of
Philosophy in Biology at the Massachusetts Institute of Technology
Abstract
Basic leucine-zipper (bZIP) transcription factors regulate many important cellular processes
including tissue differentiation, stress responses and the unfolded protein response, the cell cycle,
and apoptosis. Understanding the genes and processes regulated by bZIPs is imperative for
understanding different diseases including diabetes and cancer, but there is still much unknown
about how certain bZIPs function. Reagents capable of studying bZIP-regulated processes are
therefore needed to specifically target the proteins under study. Recent work suggests that previous
reagents, including siRNAs and dominant-negative bZIP mutants, may not have been as specific for
the target bZIP as intended. In an effort to develop new reagents capable of specifically interacting
with target bZIPs, I tested two protein design methods to determine whether they could successfully
generate tight and specific binders of the leucine-zipper coiled-coil domain.
Both methods tested take advantage of the structure and biophysical properties of a coiled coil. In
the coiled-coil dimer, two helices wrap around each other into a super helix. At the sequence level,
coiled coils have a repeating heptad sequence with seven positions denoted (a-b-c-d-e-f-g) and
typically a hydrophobic residue at a and d positions. Due to a buried hydrophobic interface
between the helices, individual coiled coils are unfolded in solution and only fold upon binding to a
partner. The first method used to design peptides that would bind tightly and specifically to bZIPs
depended on the coupled binding and folding within coiled coils. In a previous study, core positions
of the peptide pointing inward to the coiled-coil interface were optimized for stably and specifically
binding to the target, as these positions have been shown to be primarily responsible for specificity
and affinity of interactions. The solvent-exposed positions were designed to complement the core
positions. I measured the affinitiy and specificity of some of these designed peptides for their bZIP
target. I then redesigned the solvent-exposed positions to include more helix-promoting residues
that would increase the affinity of the interaction between the designed peptide and target bZIP.
Using a solution FRET assay to test both the original and redesigned peptide’s affinity for the
target and 30 off-target bZIPs, I showed that redesigning the solvent-exposed positions did stabilize
the design-target interaction from 3-fold to 90-fold but the redesign process also changed the
specificity of the peptide. The second design method reduced the full coiled-coil interaction into
interactions between individual heptads. Each heptad in the designed peptide was predicted to bind
tightly and specifically to the corresponding heptad in the target bZIP. Using this design method,
tight and very specific peptides were generated targeting different bZIPs and shown to be potent
inhibitors. Finally, I proposed how these two methods can be combined to generate more tight and
specific binders of bZIPs that can be used to reveal new insights into genes and cellular processes
regulated by bZIPs.
Thesis Supervisor: Amy Keating
Title: Associate Professor of Biology
3
Acknowledgements
I would first like to think my advisor, Amy Keating, for everything. There are too many
things to list, but thank you for your constant enthusiasm, guidance, support, advice, efforts in
turning me into a better scientific communicator, and wonderful lab environment. I have truly
enjoyed my time in the lab and I hope future scientific experiences are just as enjoyable.
I would also like to thank Bob Sauer and Thomas Schwartz for having served on my
committee since my second year. Both were very generous with comments and technical advice
during my committee meetings, and it was all much appreciated. Thank you also to Krishna
Kumar for agreeing to serve at my defense.
To the members past and present of the Keating lab: Raheleh Rezaei Araghi, Orr
Ashenberg, Judy Baek, Scott Chen, Jeremy Curuksu, Joe DeBartolo, Sanjib Dutta, Emiko Fire,
Glenna Foight, Karl Gutwin, Seungsoo Hahn, Karl Hauschild, Justin Jenson, Yong Ho Kim,
Christos Kougentakis, Chris Negron, Vladimir Potapov, Luther Reich, Aaron Reinke, Josh Sims,
Evan Thompson, Vincent Xue, and Nora Zizlsperger. Thank you all for making the lab a truly
awesome place to work. I’ve enjoyed being with you all early in the morning to late nights on
weekends and in between. Both inside and outside the lab, you’ve all been great companions. I
would especially like to thank Orr and Aaron. Both have been incredibly helpful and freely giving
of knowledge, technical expertise, and reagents, and I would likely not have reached this point
without them.
I’d also like to thank the Baker, Bell, Walker, Sauer, and Laub labs for use of equipment.
To Nathalia and Ala in the Biopolymers Facility, thank you for all the peptides and MALDI
analysis. To Debby Pheasant in the BIF, thank you for all the assistance with cleaning and building
AUC cells.
To my friends outside of lab and Biograd 2008, thank you for pulling me away from lab
and reminding me there’s more to life than science. The fun times we shared will not be forgotten.
And finally, I’d like to thank my family: my parents for always encouraging me to be
myself and pursue my interests, and my sisters for encouraging me to play with my beakers. I did it
Merm!
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Table of Contents
List of Figures……………………………………………………………………………………..9
List of Tables………………………………………………………………………………….....11
Chapter 1 Introduction…………………………………………………………………………...13
Repeat proteins…………………………………………………………………………...16
Ankyrin repeats…………………………………………………………………..16
Armadillo repeats………………………………………………………………...17
Tetratricopeptide repeat……………………………………………………….....19
Coiled-coil domain……………………………………………………………….21
Basic leucine-zipper transcription factors………………………………………………..23
FOS and JUN…………………………………………………………………….26
Activating Transcription Factor (ATF) 4 and ATF5…………………………….27
The large MAF family: MAF and MAFB……………………………………….29
X-box Binding Protein 1 (XBP1), ATF6, and CREBZF………………………...31
Targeting bZIPs………………………………………………………………………….33
Gene knockouts and knockdowns………………………………………………..33
Small-molecule inihibitors……………………………………………………….34
Rational design of dominant-negative bZIP mutants…………………………….36
Library selection of designed coiled coils……………………………………….38
Computational design of coiled coils…………………………………………….39
Experimental methods to measure coiled-coil interactions……………………………...41
Circular Dichroism (CD) spectroscopy………………………………………….41
Calorimetry………………………………………………………………………42
6
Electrophoresis mobility-shift assay (EMSA) ...………………………………...43
Coiled-coil arrays………………………………………………………………...43
References………………………………………………………………………………..46
Chapter 2 Increasing the affinity of selective bZIP-binding peptides through surface residue
redesign…………………………………………………………………………………………..55
Introduction………………………………………………………………………………56
Results……………………………………………………………………………………61
Solution characterization of original designed anti-bZIP peptides………………61
Design and testing of surface-redesigned anti-bZIP peptides……………………66
Peptides designed to bind to XBP1………………………………………………67
Peptides designed to bind to ATF6 and FOS…………………………………….73
Discussion………………………………………………………………………………..76
Additional tables…………………………………………………………………………80
Methods…………………………………………………………………………………..82
References………………………………………………………………………………..89
Chapter 3 Data-driven prediction and design of bZIP coiled-coil interactions………………….93
Introduction………………………………………………………………………………94
Results……………………………………………………………………………………96
Model benchmarking………………………………………………………….....96
Designing specific binders…………………………………………………….....97
Discussion………………………………………………………………………………106
Additional tables………………………………………………………………………..109
Methods…………………………………………………………………………………112
7
References………………………………………………………………………………119
Chapter 4 Conclusions and future directions…………………………………………………...121
Summary of design methods……………………………………………………………122
Surface-core modularity………………………………………………………...122
Heptad assembly………………………………………………………………..124
Combining heptad assembly and surface-core modularity……………………………..125
Anti-bZIPs as reagents for understanding bZIP function………………………………129
References………………………………………………………………………………132
Appendix A Characterization of original designed anti-bZIP peptides anti-LMAF, anti-LMAF-3,
anti-JUN, anti-CREB-3, and anti-CREBZF-2………………………………………………….134
Characterization of the designs by FRET………………………………………………136
Comparison to the array………………………………………………………………...137
Additional tables………………………………………………………………………..139
References………………………………………………………………………………142
Appendix B Characterization of redesigned peptides OPTanti-LMAF and OPTanti-LMAF-3..143
Characterization of redesigned peptides targeting the large MAFs…………………….144
References………………………………………………………………………………151
Appendix C Characterization of stapled and unstapled anti-bZIP peptides……………………152
Characterization of the modified-truncated peptides…………………………………...155
Characterization of a full-length modified peptide……………………………………..157
References………………………………………………………………………………160
8
List of Figures
Figure 1.1 Structures of different repeat proteins………………………………………………..16
Figure 1.2 Different views of the coiled-coil parallel dimer…………………………………….22
Figure 1.3 Canonical structure of a bZIP-DNA complex………………………………………..24
Figure 1.4 Phylogeny and families of human bZIPs……………………………………………..25
Figure 1.5 Structures of different bZIP inhibitors……………………………………………….37
Figure 1.6 Different methods to engineer specific binding to a target bZIP…………………….40
Figure 2.1 Helical-wheel diagram of a parallel 2-helix coiled coil……………………………...57
Figure 2.2 Specificity profiles of anti-FOS, anti-CREBZF, anti-ATF6, and anti-XBP1 at 37
°C………………………………………………………………………………………………...64
Figure 2.3 CD spectra of the designed peptides anti-XBP1, OPTanti-XBP1_A, and OPTantiXBP1_B………………………………………………………………………………………….68
Figure 2.4 Characterization of OPTanti-XBP1_A and OPTanti-XBP1_B by FRET……………70
Figure 2.5 Characterization of OPTanti-XBP1_B-GLN………………………………………...72
Figure 2.6 Net charges of bZIPs used in the study………………………………………………73
Figure 2.7 Characterization of OPTanti-ATF6 and OPTanti-FOS……………………………....75
Figure 2.8 Controls showing the peptides anti-FOS and OPTanti-FOS inhibit FOS-JUN binding
to DNA…………………………………………………………………………………………...77
Figure 3.1 Cartoon of the model bZIP GCN4 as interacting heptads……………………………94
Figure 3.2 Highlights of the scoring method used in the design process………………………..98
Figure 3.3 Specificity profiles at 37 °C of the designs anti-XBP1, anti-JUN, anti-ATF4, and antiATF5……………………………………………………………………………………………103
Figure 3.4 Lack of inhibition of off-target bZIP complexes by unlabeled anti-ATF4 at 37 °C..105
Figure 3.5 Designed peptides acting as inhibitors of target bZIP complexes……………..........106
Figure 4.1 CD wavelength scan and thermal melt for the original and capped anti-LMAF-3
peptides…………………………………………………………………………………………127
9
Figure A.1 Interactions detected in the FRET assay and not on the array……………………...139
Figure B.1 Comparing the affinities of interactions made by ant-LMAF and OPTanti-LMAF at
23 °C……………………………………………………………………………………………146
Figure B.2 Helical-wheel diagrams of the original and surface-redesigned anti-LMAF
homodimeric interaction……………………………………………………………………….146
Figure B.3 Comparing the affinities of interactions made by ant-LMAF-3 and OPTanti-LMAF-3
at 23 °C…………………………………………………………………………………………148
Figure B.4 Helical-wheel diagrams of the original and surface-redesigned anti-LMAF3
interactions with ATF4………………………………………………………………………....148
Figure C.1 Structure of the non-natural amino acid used to form the hydrophobic staples……154
Figure C.2 CD thermal melt of aFT1 with FOS………………………………………………...154
Figure C.3 Modified peptides acting as inhibitors of FOS-JUN dimerization…………………157
Figure C.4 Unmodified and unstapled anti-FOS designs inhibiting FOS-JUN dimerization…..158
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List of Tables
Table 2.1 Sequences of designed peptides and bZIPs targeted………………………………….63
Table 2.2 Measured affinities between designed peptides and bZIP targets…………………….64
Table 2.3 Fitted masses of design-target complexes from analytical ultracentrifugation……….66
Table 2.4 Sequence properties of designed peptides…………………………………………….67
Table 2.5 Measured affinities for designed peptides at 37 °C…………………………………...80
Table 2.6 Measured affinities for designed peptides at 23 °C…………………………………...80
Table 2.7 Measured affinities for designed peptides at 4 °C………………………………….....81
Table 2.8 Calculated affinities for OPTanti-XBP1_A at 37, 23, and 4 °C in 400 mM KCl…….81
Table 2.9 Calculated affinities for OPTanti-XBP1_B at 37, 23, and 4 °C in 400 mM KCl….….82
Table 2.10 Calculated affinities for OPTanti-XBP1_B-GLN at 37, 23, and 4 °C…………….…82
Table 3.1 Comparison of previously published predictive models to the new machine-learning
model. ………………….………………….………………….……………………………….....97
Table 3.2 Sequences of bZIP targets and designed peptides used in this study…………………99
Table 3.3 Tested anti-bZIP designed peptides and calculated affinities. ………………………100
Table 3.4 Molecular weights of the design-bZIP complexes determined by analytical
ultracentrifugation. ………………….………………….………………….…………………...101
Table 3.5 Calculated FRET efficiencies and affinities of design-target interactions. …………101
Table 3.6 Calculated inhibition constants (KI) for the designed peptide inhibiting a FRET
complex of the design-target interaction. ………………….………………….………………..102
Table 3.7 Affinities determined with anti-ATF4 as the FRET donor. …………………………104
Table 3.8 Calculated affinities at 37 °C. ………………….………………….………………...109
Table 3.9 Calculated affinities at 23 °C. ………………….………………….………………...110
Table 3.10 Calculated affinities at 4 °C. ………………….………………….………………...111
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Table 4.1 Frequency of appearance of a given amino acid at the d position in the N-terminal,
middle, or C-terminal heptad. ………………….………………….…………………………...128
Table 4.2 Frequency of appearance of a given amino acid at the a position in the N-terminal,
middle, or C-terminal heptad. ………………….………………….…………………………...129
Table A.1 Sequences of the intended targets and designs that were tested. …………………...135
Table A.2 Calculated affinities of design-target interactions. ………………….……………...136
Table A.3 All calculated Kd’s for anti-LMAF at 37, 23, and 4 °C. ……………………………139
Table A.4 All calculated Kd’s for anti-LMAF-3 at 37, 23, and 4 °C…………………………...140
Table A.5 All calculated Kd’s for anti-JUN at 37, 23, and 4 °C.……………………………….140
Table A.6 All calculated Kd’s for anti-CREBZF-2 at 37, 23, and 4 °C. ……………………….141
Table A.7 All calculated Kd’s for anti-CREB-3 at 37, 23, and 4 °C…………………………...141
Table B.1 Sequences for the original and surface-redesigned peptides targeting MAF. ………145
Table B.2 Affinities of redesigned anti-LMAF peptides for the target MAF family…………..145
Table B.3 All calculated Kd’s for OPTanti-LMAF at 37, 23, and 4 °C. ……………………….149
Table B.4 All calculated Kd’s for OPTanti-LMAF-3 at 37, 23, and 4 °C. …………………….150
Table C.1 Sequences of truncated native and designed coiled-coil peptides tested for interaction
with full-length bZIPs. ………………….………………….………………….……………….154
Table C.2 Three modified versions of aFT1. ………………….……………………………….155
Table C.3 Calculated MRE at 222 nm for the original and substituted aFT1 peptides. ……….156
Table C.4 Original and modified anti-FOS sequences. ………………….……………………...158
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Chapter 1
Introduction
13
Proteins perform the majority of work that occurs within a cell, often as multi-protein
complexes (Cochran 2000). With the sequencing of genomes, it was estimated that the number of
functional protein-protein interactions in E. coli is greater or equal to 6,800, and for S. cerevisiae
estimates exceed 45,000 (Marcotte et al. 1999). In humans, this number is estimated to be around
130,000 (Venkatesan et al. 2009). Many high-throughput studies looking at large networks of
interactions indicated that protein-protein interactions are generally specific (Gavin et al. 2001,
Ito et al. 2001, Newman and Keating 2003, Rual et al. 2005, Stiffler et al. 2007, and Reinke et al.
2013). Proteins bind tightly to a select few partners, and understanding how these specific
interactions occur, or inhibiting these interactions, would allow for a greater understanding of
their functional consequences. However, inhibition of protein-protein interactions is often
difficult due to the large surface area buried in a typical interface and the high affinity between
many interacting proteins. Typical interfaces bury about 1,600 Å2 and have, on average, an
amino-acid composition similar to that of a protein surface (Lo Conte et al. 1999). Although
there are many reports of drug-like molecules binding to protein surfaces, particularly at enzyme
active sites, there are fewer reports about drug-like molecules disrupting protein-protein
interfaces (Cochran 2000).
Many groups have turned to engineering protein-based drugs that are capable of binding
to protein surfaces and inhibiting interactions. Recombinant antibodies have been engineered or
selected for binding to different targets including kinases (Lu et al. 2001) and growth receptors
(Heitner et al. 2001), but the knowledge gained from these studies is limited due to the fact that
different antibody-epitope interactions have different conformations. Thus, studying one
particular antibody-peptide complex does not necessarily lead to general principles that can be
applied to other complexes (Parmeggiani et al. 2008).
14
Other protein molecules engineered or designed to bind other proteins and inhibit proteinprotein interactions have taken advantage of small, native protein scaffolds like the PDZ and
SH3 domains. These scaffolds have been engineered for binding short peptides (Reina et al. 2002
and Smith et al. 2013) and have the advantage over antibodies of utilizing a common binding
mode within a particular protein family. However, the recognition sequences of these domains
are short, making the binding affinity between a domain and its target peptide low. It may be
possible to link some of these domains together to recognize longer peptide sequences, but it is
not known if the linked domains can be adapted for recognition of arbitrarily long sequences
with associated increases in affinity. Additionally, the flexibility inherent in linking the
constructs would not be entropically favorable and could lead to lower binding affinities
(Parmeggiani et al. 2008).
To obtain high-affinity binding that the smaller scaffolds lack, some scientists have
recently been turning to repeat proteins. A repeat protein consists of a tandem array of a smaller
structural motif (Grove et al. 2008). Different examples of repeat protein structures are shown in
Figure 1.1. Generating a consensus module for a particular repeat allows linking of an arbitrary
number of modules for recognition of a specific protein (Binz et al. 2003, Stumpp et al. 2003,
Main et al. 2003, and Parmeggiani et al. 2008). Repeat proteins generally have a large surface
area of interaction and interact with the ligand in an extended fashion to maximize the surface
area of contact per amino acid (Grove et al. 2008). Below, I describe a few different repeat
proteins and how groups have used them to design novel binders and inhibitors of protein-protein
interactions. I then introduce the idea of the coiled-coil domain as a structural motif that, like a
repeat protein, can be thought of as a composition of different units that can be modulated.
15
Figure 1.1
(a)
(b)
(c)
Figure 1.1 Structures of different repeat proteins. (a) Structure of GAPβ1 (PDB: 1AWC from Batchelor et al.
1998) showing the characteristic ankyrin repeat. (b) Structure of importin-α (PDB: 1EE5 from Conti and Kuriyan
2000) showing the characteristic armadillo repeat. (c) Structure of the tetratricopeptide repeats of protein
phosphatase 5 (PDB: 1A17 from Das et al. 1998). One repeat within each protein is colored in magenta.
Repeat proteins
Ankyrin repeats
One common repeat protein found in nature is the ankyrin repeat. There are more than
2,000 ankyrin repeat proteins, with over 14,000 different ankyrin repeats (Letunic et al. 2002).
These proteins are found both inside and outside of cells and in the cell membrane, and carry out
many diverse functions including acting as transcriptional regulators, toxins, and cytoskeletal
organizers. As shown in Figure 1.1a, the ankyrin repeat consists of a pair of adjacent anti-parallel
alpha helices connected by β-turns and is on average 33 amino acids long. Typically, four to six
repeats stack together to form a domain (Sedgwick and Smerdon 1999). Binding between an
ankyrin repeat protein and a target protein occurs via the protruding β-turns and the following
alpha helices. Multiple repeats within the protein contact the target protein, and this interaction
mode contributes to high-affinity interactions (Binz et al. 2003).
16
Work by Pluckthun and colleagues has focused on using ribosome display to select novel
ankyrin repeat proteins that bind to important therapeutic targets including bacterial kinases that
confer resistance to antibiotics (Amstutz et al. 2005), members of the MAP kinase pathway
(Amstutz et al. 2006), the extracellular domain of the epidermal growth factor HER2 (Zahnd et
al. 2007), the plus end of a microtubule (Pecqueur et al. 2012), and ERK kinase (Kummer et al.
2012). In ribosome display, a library of mRNA molecules encodes different ankyrin repeats,
each with different amino acids at locations that potentially interact with the target protein. Each
mRNA molecule also lacks a stop codon. This fuses the translated protein to the tRNA and the
mRNA stays connected to the ribosome, thereby linking genotype and phenotype via the
ribosome (Hanes et al. 2000). Using this technique, translated proteins that bind their targets with
affinities in the picomolar to low nanomolar range have been obtained.
Some engineered ankyrin repeat proteins have also been shown to be highly specific. A
protein selected for binding to the MAP kinase JNK2 did not bind to JNK1, a related kinase with
86% sequence similarity (Amstutz et al. 2006). Additionally, ankyrin repeat proteins selected for
binding to the MAP kinase ERK distinguished between the inactive and active (phosphorylated)
form of the protein by recognizing a change in conformation of the activation loop. These
proteins also maintained their specificity within cells (Kummer et al. 2012), indicating that
ankyrin repeat proteins are capable of obtaining the selectivity and affinity of drug-like
molecules.
Armadillo repeats
Recently, the Pluckthun group has begun work on a different type of repeat protein.
Armadillo repeat proteins are characterized by tandem repeats of a 42-amino acid motif
17
originally identified in the Drosophila melanogaster segment polarity gene Armadillo.
Armadillo repeats were later identified in many different proteins that carry out diverse functions
including transcription, cell adhesion, and tumor suppressor activity (Peifer et al. 1994). As
shown in Figure 1.1b, the domain forms a right-handed super helix. Every repeat is composed of
three alpha helices denoted H1, H2, and H3, and multiple repeats stack together to form a
domain. Specialized repeats at the N- and C-terminus protect the hydrophobic core from
exposure to solvent (Madhurantakam et al. 2012).
Armadillo repeats can bind many different peptides, but do so in a conserved manner.
Structures indicate that most peptides bind in an extended conformation along the surface of a
groove formed by the H3 helices. A conserved asparagine residue at the C-terminus of H3 forms
a hydrogen bond to the backbone of the binding peptide. This interaction helps maintain the
peptide in an extended conformation, with neighboring amino acids forming additional
interactions with the peptide’s side chains. Because of this precise network of interactions, each
repeat in the domain specifically recognizes two amino acids in the target protein (Parmeggiani
et al. 2008).
The use of multiple stacked repeats that each bind to specific residues in a target peptide
allows an armadillo repeat protein to specifically and tightly bind its ligand, with reported
affinities in the low nanomolar range (Catimel et al. 2001). To take advantage of this, the
Pluckthun group generated three consensus internal armadillo repeats based on sequence
alignments of armadillo repeat proteins from the subfamilies β-catenin/plakoglobin, importin-α,
and a combination of both subfamilies (Parmeggiani et al. 2008). They argued that consensus
repeats were better as a starting unit for library generation compared to choosing a particular
repeat to randomize. They chose to generate a consensus repeat because they thought it would
18
increase the stability of the protein to incorporate structural features common in protein families
but potentially absent in individual family members (Boersma and Pluckthun 2011).
Consensus armadillo modules were used as a scaffold in a ribosome display experiment.
In this experiment, the residues important for maintaining the repeat structure were held constant
and the residues known to be important for contacting the target peptide were randomized
(Varadamsetty et al. 2012). Using this technique, a novel armadillo repeat protein was selected
for specifically binding to a chosen target peptide, with a reported equilibrium dissociation
constant around 7 µM. Future work selecting for tightly-binding armadillo repeat proteins may
lead to new proteins with lower dissociation constants.
Tetratricopeptide repeats
The tetratricopeptide repeat (TPR) is composed of 34 amino acids. Each repeat forms two
anti-parallel alpha helices, and repeats stack together in a parallel array to form an extended
molecule with a super-helical structure (Main et al. 2003), as shown in Figure 1.1c. The number
of tandem repeats can vary from 3-16, with three being the most common number of repeats.
TPR-containing proteins are present in both prokaryotes and eukaryotes and are involved in a
variety of processes including cell cycle regulation, protein folding, and neurogenesis (D’Andrea
and Regan 2003).
The super-helical structure of TPR proteins forms a pair of convex and concave surfaces,
with ligands usually binding to the concave surface. Unlike armadillo repeats, where specific
residues in the repeat recognize specific residues in the ligand, there is no conserved rule for
TPR proteins, and many different types of ligands can bind. Different types of residues can be
accommodated, and the nature of the surface residues, whether charged or hydrophobic, can
19
dictate the precise ligand that binds. In general, binding specificity of TPRs cannot be attributed
to a single force (ie electrostatic interactions), but instead it is usually a combination of different
forces that allows for high specificity (Zeytuni and Zarivach 2013).
One naturally occurring TPR system that has been mimicked using engineered proteins is
the TPR1 and TPR2A domains of Hsp Organizing Protein (HOP), a co-chaperone of Hsp70 and
Hsp90 that brings the proteins together and is essential for the proper folding of oncogenic
proteins (Cortajarena et al. 2008). The TPR1 and TPR2A domains have dissociation constants of
50 and 5 µM for Hsp70 and Hsp90, respectively. For designing new TPRs, a consensus TPR
module was generated from an alignment of TPR sequences and statistical analysis of aminoacid residue preferences at each position in the TPR (Main et al. 2003). To understand
contributions of particular residues to binding affinity and specificity, the Hsp90-binding
residues from TPR2A were grafted onto a consensus TPR. This engineered TPR bound more
weakly to the C-terminal peptide of Hsp90 than the original TPR2A, with a dissociation constant
of 200 µM, but the engineered domain was specific for Hsp90 over Hsp70 (Cortajarena et al.
2004). In a later study, this protein was re-engineered to optimize the electrostatic interactions
between the designed TPR and Hsp90, providing a final dissociation constant of 1 µM
(Cortajarena et al. 2008). This final engineered TPR was tested in vivo for inhibition of Hsp90
folding activity in HER2 positive breast cancer cells. One quarter of breast cancer cells
overexpress the growth factor receptor HER2, and proper folding and maturation of HER2 is
dependent on Hsp90 (Slamon et al. 1987). However, introduction of the engineered TPR into the
cells reduces HER2 levels and inhibits cell proliferation, indicating that TPR proteins can
function as drug-like molecules (Cortajarena et al. 2008).
20
Coiled-coil domains
Unlike ankyrin, armadillo, and TPR proteins, coiled coils are not typically thought of as
repeat proteins, but the repeating structure and conserved binding mode of coiled coils are
characteristic of repeat proteins. Coiled coils are not as versatile as other repeat proteins that bind
to many different types of proteins or DNA, as they are limited to binding to other coiled coils.
But due to their wide prevalence as an interaction motif, their relatively small structures, and
their experimental tractability, much is known about how they interact (Lupas 1996). Coiled
coils are found in structural proteins, transcription factors, and enzymes, and are involved in
maintaining multi-protein complexes.
Structurally, coiled coils are bundles of alpha helices that wind around each other and
form a super helix (Figure 1.2a). The bundle can contain two to five helices. The helices can be
aligned in a parallel formation (N to N and C to C) or in an anti-parallel formation (N to C)
(Lupas 1996). In this thesis, the discussion will focus mainly on parallel dimeric coiled coils.
Coiled coils are structurally distinguished from other helical bundles based on the sidechain packing of the core residues. This packing scheme is referred to as “knobs-into-holes,” and
was first described by Crick (Crick 1952). Here, one side chain on one helix (the “knob”) packs
into a hole formed by the side chains of four residues (the “hole”). This regular packing scheme
requires that all core positions occupy the same position with respect to an interacting helix after
each helical turn. Thus, the coiled-coil super helix reduces the number of residues per turn to 3.5
to allow the side chains to repeat after two turns, or seven residues. These repeating seven
residues, or heptads, are denoted as (abcdefg)n, with core residues occupying the a, d, e, and g
positions and facing towards the interface between the helices (Figure 1.2b) (Lupas 1996).
21
This regular repetition of units, either viewed as layers of core residues or as repeating
units of full heptads (Figure 1.2c), is analogous to the small repeating helices observed in TPRs
and ankyrin repeats. Additionally, the long extended interface formed between two coiled-coil
chains is characteristic of repeat proteins. These long interfaces allow for maximum contact
between the helices, and dissociation constants for native and designed coiled-coil dimers have
been reported to be in the subnanomolar to micromolar range (O’Shea et al. 1993, Wendt et al.
1995, Reinke et al. 2013, and Thomas et al. 2013).
Figure 1.2
(a)
(b)
(c)
Figure 1.2 Different views of the coiled-coil parallel dimer. (a) Structure of GCN4 (PDB: 2ZTA from O’Shea et
al. 1991). (b) Helical-wheel diagram showing the view looking down the helical axis of a dimer. Core residues are
boxed in blue, solvent-exposed residues are circled in gray. Arrows indicate the a-a’, d-d’, and e-g’ interactions that
are referred to throughout this thesis. A prime indicates a position on the partner chain. (c) Cartoon view of a coiledcoil dimer showing the helices as repeating heptad units. Arrows denote the common g-e’i+1 interaction.
To an even greater extent than for the armadillo repeat protein, much is known about how
specific residues within the coiled coil contribute to the binding interaction. Within each heptad,
22
hydrophobic residues typically occupy the a and d positions, and many double-mutant cycles
from the Vinson lab have shown how certain hydrophobic pairings at the a-a’ positions
contribute to dimer stability (Acharya et al. 2002 and Acharya et al. 2006). Residues in the e and
g positions are usually occupied by charged amino acids and can form salt bridges that pack
against the hydrophobic core. These residues also contribute to the stability and specificity of
interactions, and other work from the Vinson lab has determined the energetic contributions to
dimer stability of certain e-g’ pairs (Krylov et al. 1994).
One particular protein family incredibly reliant on forming complexes via coiled coils is
the basic leucine-zipper (bZIP) family of transcription factors. In the next section, I introduce
what bZIPs are and their importance in many different aspects of biology.
Basic leucine-zipper (bZIP) transcription factors
bZIP transcription factors are found throughout the animal, plant, and fungal kingdoms
(Vinson et al. 2006). Structurally, bZIPs have an N-terminal basic region with two clusters of
basic residues that facilitate sequence-specific DNA binding (Figure 1.3). C-terminal to the basic
region is the coiled-coil dimerization motif (the “zipper”) with leucine residues usually
occupying the d position of the heptad (Vinson et al. 2002). bZIPs can form homo- or
heterodimers, and which dimer forms dictates which sequence of DNA is bound. The ability of a
particular dimer to recognize a specific DNA site is independent of the zipper domain and is only
specified by the residues in the basic region and the “linker” region between the basic region and
the beginning of the zipper domain (Agre et al. 1989).
23
Figure 1.3
Figure 1.3 Canonical structure of a bZIP-DNA complex. Structure of the FOS-JUN heterodimer bound to DNA
(PDB ID: 1FOS from Glover and Harrison 1995). The basic region is colored in magenta and the zipper coiled-coil
domain is in green.
Fifty-three different bZIPs have been identified in humans, and many bZIPs have closely
related sequences (Figure 1.4a). These 53 bZIPs can be grouped into families based on the
dimerization properties of the zipper domain. Families were classified into three functional
groups based on the preferences of members to form homo- or heterodimers or both (Figure
1.4b). A family’s functional classification was based on simple rules (Vinson et al. 2002). For
example, the presence of charged residues at e and g positions allows electrostatic interactions to
occur between core residues, and similarly charged residues disfavor homodimerization. This ge’i+1 interaction is depicted in Figure 1.2c. Conversely, the presence of an asparagine residue at
an a position favors homodimerization, as the interaction between asparagine and a hydrophobic
residue on the neighboring chain is very energetically unfavorable (Acharya et al. 2002).
Interestingly, these simple dimerization “rules” were supported by later studies looking at pairwise interactions between human bZIPs (Newman and Keating 2003 and Reinke et al. 2013),
24
indicating that binary predictions about whether two bZIPs can interact can be roughly estimated
by looking at the energetic contributions determined by the Vinson lab of possibly interacting
residues (Krylov et al. 1994 and Acharya et al. 2006).
Figure 1.4
(a)
(b)
Figure 1.4 Phylogeny and families of human bZIPs. (a) Phylogenetic tree of the 53 human bZIPs. Alignment was
based on the basic regions and leucine-zipper domains. The dendogram was generated with maximum-likelihood
using Phylogeny.fr (http://www.phylogeny.fr from Dereeper et al. 2008). (b) The different families of bZIPs with
their members. Italicized family names preferentially homodimerize. Underlined family names preferentially
heterodimerize. Family names both underlined and italicized can homo- and heterodimerize.
bZIPs regulate many important cellular processes. Below, I describe different potential
25
bZIP targets and their relevance in biological processes. In my thesis, I worked on designing
peptide inhibitors to target some of the bZIPs discussed below.
FOS and JUN
Both fos and jun are inducible genes activated in response to mitogens and differentiation
factors (Muller et al. 1984 and Kruijer et al. 1984). The FOS family has four members: FOS,
FRA2, FOSL1, and FOSL2. The JUN family, also called AP-1, has three members: JUN, JUNB,
and JUND (Vinson et al. 2002). Activation of genes by FOS proteins requires dimerization with
JUN or other bZIP proteins, and which heterodimer forms dictates the effects on the cell
(Ransome and Verma 1990). JUN proteins can also form homodimers, but the affinity of JUN
proteins for DNA sequences is increased in the presence of FOS proteins due to the increased
stability of the FOS-JUN heterodimer over the JUN homodimer (Kouzarides and Ziff 1989 and
O’Shea et al. 1989).
Both fos and jun are oncogenes, and misregulation of the gene products leads to cellular
transformation (Curran et al. 1984 and Schutte et al. 1989a). Work by Schuermann demonstrated
that the leucine-zipper domain of FOS, which is required for dimerization with JUN, is also
required for cellular transformation by JUN (Schuermann et al. 1989). This was consistent with
the hypothesis that the FOS and JUN proteins participate in cellular transformation through
constitutive activation of their normal functions. However, co-transfection of JUNB with JUN
and FOS leads to a decrease in transformation and transactivation, indicating that different
members within the same family can modulate each other’s actions (Schutte et al. 1989b).
The role of AP-1 in cell proliferation is well documented. Double knockouts of
FOS/FOSB and knockouts of JUN in mouse fibroblasts reduce the expression of cyclin D1
26
(Brown et al. 1998 and Wisdom et al. 1999), a protein necessary for the G1 to S phase transition
of the cell cycle. Additionally, JUN stimulates cell cycle progression through repression of
p21Cip1, an inhibitor of the G1 to S phase transition, by reducing the activity of the tumor
suppressor gene p53 (Shaulian et al. 2000).
Consistent with the early work by Schutte et al., later studies indicated that JUNB
antagonizes the stimulatory effects of JUN by repressing the cyclin D1 promotor (Bakari et al.
2000). However, further work indicated that replacement of JUN by JUNB prevents cellular
defects that occur in a knockout of jun, suggesting that in the absence of JUN, JUNB can
function as a positive growth regulator (Passegue et al. 2002). These data suggest that the
differences in JUN and JUNB function can be cell-specific and dependent on the relevant
concentrations of these proteins.
To further complicate matters, both FOS and JUN induce apoptosis under certain
conditions via activation of Fas-ligand transcription. Inhibition of JUN and FOS function by
RNAi and a dominant-negative mutant of JUN increases the survival of growth factor-deprived
lymphoid cells and neuronal cells, respectively (Colotta et al. 1992 and Ham et al. 1995). It is
believed that the balance of pro- and anti-apoptotic factors regulated by AP-1 determines
whether the cell lives or dies, and this balance can vary from one cell to another depending on
environmental conditions that can enhance or down-regulate the activity of AP-1 (Shaulian and
Karin 2002). Therefore, the importance of these proteins in regulating cell death and growth
makes the design or engineering of reagents capable of studying their interactions in vivo highly
valuable.
Activating Transcription Factor (ATF) 4 and ATF5
27
ATF4 and ATF5, which are both members of the ATF4 family, can form homo- and
heterodimers with themselves and other bZIP proteins (Reinke et al. 2013). Their functional
heterodimerizing partners include members of the FOS, JUN, and C/EBP families (Hai and
Hartman 2001).
ATF4 is a stress response gene and is upregulated by several stressors including oxygen
deprivation (hypoxia and anoxia), amino-acid deprivation, oxidative stress, and ER stress (Ameri
and Harris 2008). ATF4 was originally characterized as a transcriptional repressor (Karpinski et
al. 1992), but it can also activate E-selectin (Liang and Hai 1997), asparagine synthesase (Chen
et al. 2004), and VEGF (Roybal et al. 2005).
Hypoxia and anoxia, deprivation of oxygen to an organ or tissue, are important stress
factors relevant to cancer progression. ATF4 levels have been shown to be elevated in primary
human tumors compared to normal tissues (Ameri et al. 2004). Other aspects of a tumor
microenvironment, including ER and oxidative stress, also upregulate ATF4. Additionally, a
high level of ATF4 within cancer cell lines makes the cells resistant to cisplatin, an anti-cancer
drug used to successfully treat solid tumors (Tanabe et al. 2004). Thus, having a molecule that
can inhibit ATF4 function could prove to be useful for better understanding the mechanisms by
which tumor cells become drug resistant.
Like ATF4, ATF5 has been shown to be more highly expressed in cancerous tissues than
in normal tissues (Monaco et al. 2007). ATF5 expression is particularly high in glioblastoma, an
aggressive form of malignant glioma, and elevated levels of ATF5 expression in those tumors
have been associated with shorter survival time (Sheng et al. 2010a). ATF5 has been shown to
play a role in cell survival, proliferation, and cellular differentiation depending on its cellular
context. ATF5 levels fall in lymphocytes and HeLa cells after withdrawal of trophic support,
28
leading to apoptosis. To support ATF5’s role in cell survival, it was shown that constitutive
expression of ATF5 promotes survival, and transfection with a dominant-negative ATF5 lacking
the transcriptional activation domain induces apoptosis in HeLa cells (Persengiev et al. 2002).
Although in this study modulation of ATF5 levels did not affect the cell cycle, ATF5 does
regulate proliferation of neuroprogenitor cells and oligodendrocytes (Angelastro et al. 2003 and
Mason et al. 2005). In differentiating cells, ATF5 levels have been shown to increase during the
early phase of chondrogenesis (Shinomura et al. 2006) and during embryonic stem cell
differentiation to embryoid bodies (Sampath et al. 2008), suggesting that induction of ATF5
contributes to the differentiation process.
Due to its prevalence in many cancers, pharmacological inhibitors of the MAP kinases
that activate ATF5 are available (Sheng et al. 2010a). In terms of targeting ATF5 directly,
inhibition of ATF5 by a dominant-negative mutant of ATF5 lacking the basic region selectively
kills breast cancer cells but not normal breast epithelial cells (Monaco et al. 2007). Additionally,
RNAi-mediated knockdown of ATF5 induces apoptosis in tumor cell lines derived from lung,
prostate, skin, and ovary tissue (Sheng et al. 2010b), indicating that like ATF4, more molecules
that can selectively inhibit ATF5 could be useful for further understanding how this bZIP
contributes to cancer progression.
The large MAF family: MAF and MAFB
The large MAF proteins differ from the small MAF proteins due to the presence of a
transactivation domain at the N-terminus (Fujiwara et al. 1993). The large MAFs differ from
other related bZIP families like JUN and FOS due to the presence of an extended homology
29
region N-terminal to the basic region that contributes to DNA binding (Kerpolla and Curran
1994).
Like the other bZIPs discussed so far, the large MAFs have been shown to increase cell
proliferation in primary fibroblasts via control of cell-cycle progression (Pouponnot et al. 2006),
and cyclin D2 is a target of the MAF bZIP (Hurt et al. 2004). The primary responsibility of the
large MAFs is to activate tissue-specific genes during terminal differentiation of cells. The
expression of the large MAFs is tightly regulated, and different members are expressed at
specific times during tissue development. This is readily seen in the developing mouse lens.
MAF and MAFB are expressed in the lens fiber and lens epithelial cells, respectively (Reza and
Yasuda 2004), and MAF knockout mice exhibit microphthalmia due to the reduction or loss of
expression of the α-, β-, and γ-crystallins (Kim et al. 1999a).
Tight regulation of large MAF expression is imperative for the cell, as MAF genes are
overexpressed in 60% of human angioimmunoblastic T-cell lymphomas (Morito et al. 2006) and
in 50% of multiple myeloma cases (Hurt et al. 2004). The majority of what is known about how
the large MAFs contribute to oncogenic transformation comes from studies on multiple
myeloma.
Multiple myeloma is a disease of antibody-secreting mature B cells that arises in the bone
marrow. As the disease progresses, the tumor cells migrate, and detection of multiple myeloma
cells in the blood is the onset of plasma cell leukemia, the terminal phase of multiple myeloma
(Eychene et al. 2008). Translocations of MAFB that cause its overexpression are usually seen in
the early stages of multiple myeloma, whereas MAF translocations are seen in the more
advanced stages of the disease (Chng et al. 2007).
In studies of the more advanced stages, MAF causes deregulation of genes associated
30
with cell invasion and metastasis (Suzuki et al. 2004). In primary fibroblasts, post-translational
phosphorylation of MAF causes it to deregulate the expression of genes involved in extracellular
matrix remodeling and cell invasion. Only a subset of MAF-regulated genes are affected by
MAF phosphorylation, leading to the hypothesis that phosphorylated MAF is not required for
tumor initiation but is required for later stages like invasion and metastasis (Rocques et al. 2007).
GSK3, a kinase that phosphorylates MAF, is a drug target for multiple myeloma patients (Cohen
and Goedert 2004); however, having a general MAF inhibitor for MAF-overexpressing cells
would be beneficial for determining the roles of MAF during different stages of cancer.
X-box Binding Protein 1 (XBP1), ATF6, and CREBZF
The bZIPs XBP1, ATF6, and CREBZF are all members of different families, but
alignment of the basic regions and leucine-zipper domains indicates that they have closely
related sequences (Vinson et al. 2002). There is little known about the functional roles of
CREBZF, but recent in vitro work indicated that CREBZF interacts very tightly with XBP1 and
ATF6B, a member of the ATF6 family, which could be suggestive of possible roles within the
cell (Reinke et al. 2013).
Unlike CREBZF, much is known about ATF6 and XBP1, especially regarding their
involvement in the unfolded protein response during ER stress. Stress in the ER resulting from
an imbalance between protein folding load and the ER capacity occurs under many
circumstances including glucose deprivation, viral infection, and genetic mutations in secretory
proteins (Rutkowski and Kaufman 2004). ER homeostasis is maintained via a collection of
different signaling pathways collectively known as the unfolded protein response (UPR). There
31
are three main mechanisms of the UPR, two of which directly involve the bZIPs ATF6 and
XBP1 (Hollien 2013).
The role of XBP1 was discovered in studies showing that the ER transmembrane protein
IRE1 oligomerizes upon sensing ER stress, leading to IRE1 autophosphorylation and formation
of an active endoribonuclease on the cytosolic side of the ER membrane (Cox and Walter 1996).
The active form of IRE1 splices the intron out of the mRNA encoding XBP1, which is then
translated. The XBP1 protein activates the expression of BiP, the major ER chaperone (Travers
et al. 2000).
In the second mechanism, ATF6, a resident ER protein, is transported to the Golgi for
cleavage upon ER stress. The ER chaperone BiP binds ATF6 and blocks its Golgi localization
signal (Shen et al. 2002). Under stress, BiP dissociates from ATF6, allowing ATF6 to be
transported to the Golgi where it is cleaved by the two proteases S1P and S2P to its active,
functional form (Ye et al. 2000). Once active, ATF6 induces the expression of many ERresident chaperones including BiP, protein disulfide isomerase, and Grp94 (Okada et al. 2002).
Due to their prominent roles in the UPR and in regulating ER homeostasis, misregulation
or deletion of ATF6 and XBP1 can be detrimental to the cell. Like the large MAF proteins, the
IRE1-XBP1 pathway has been shown to be very important in plasma cell differentiation. When
the B-cell lymphoblast differentiates into a plasma cell, there is a massive ER expansion that
occurs to allow the cell to develop the capacity to eventually secrete antibodies in large
quantities. It has been shown that XBP1-deficient mature B cells fail to differentiate into plasma
cells (Reimold et al. 2001). Additionally, XBP1 plays a role in lymphoid cancers, and the loss of
XBP1 has been shown to severely inhibit tumor growth in multiple myeloma (Romero-Ramirez
et al. 2004). XBP1 is considered to be one of the most promising targets of the UPR, and
32
although there are inhibitors of IRE1 available and other treatments for multiple myeloma
patients, there is currently no cure (Mimura et al. 2012); thus new treatments potentially
targeting XBP1 itself would be therapeutically valuable.
Targeting bZIPs
Gene knockouts and knockdowns
Due to the prominent roles of bZIPs in regulating expression of genes involved in cell
growth and differentiation, knockouts of bZIPs are often lethal to the cell. A complete knockout
of the jun locus in mice causes embryonic death 11 to 12 days post-coitus, indicating the
importance of JUN in development after the mid-gestation period. Embryonic fibroblast cell
lines derived from these animals also exhibit poor growth in cell culture (Johnson et al. 1993).
Fos-deficient mice survive to birth, but their survival rate is significantly reduced. Weeks after
birth, these mice display defects in bone growth and gametogenesis and a reduced response to
external stimuli (Johnson et al. 1992), implicating FOS in all of these processes.
Knockouts in specific cell types have revealed some of the roles of the large MAF bZIPs.
Knockouts of mafa in pancreatic β-cells cause diabetes mellitus due to lack of insulin production
(Zhang et al. 1995); knockouts of mafb in myeloid progenitor cells cause altered actin-dependent
macrophage morphology (Aziz et al. 2006); and knockouts of maf in T cells cause a lack of IL4
production and embryonic lethality (Kim et al. 1999b).
When gene knockouts are lethal to the cell, RNA-mediated interference, or RNAi, is an
effective tool to reduce gene expression and gain an understanding of the functional
consequences of under-expressing a gene. With RNAi knockdown, a small interfering RNA
molecule (siRNA) is introduced into the cell to selectively bind to the mRNA of the gene under
33
study and inhibit its translation. It is a technique that has been used to study bZIP function, and
much of the information known about bZIP-regulated processes was learned using RNAi.
RNAi knockdowns indicated that both FOS and JUN are involved in the early stages of
apoptosis induced by growth factor deprivation of lymphoid cells (Colotta et al. 1992); that MAF
binds to the glucagon promotor in pancreatic α-cells and is necessary for its full promotor
activity (Kataoka et al. 2004); that ATF4 is required for transcription of genes encoding proteins
involved in amino-acid anabolism (Adams 2007); that XBP1 plays a major role in cellular
resistance to oxidative stress in HeLa cells (Liu et al. 2009); and that MAFB represses
transcription of interferon type Iβ (Kim and Seed 2010).
RNAi is a powerful technique, but it is only useful if the siRNA sequence being used is
highly specific for the given gene target. People have found that several mismatches between the
siRNA and a target RNA can be tolerated. Snove and Holen investigated 359 published siRNA
sequences and found that 75% elicited non-specific effects by binding to off-target mRNAs
(Snove and Holen 2004). Therefore, rigorous controls and careful inspection of co-expressed
mRNA sequences are needed in order to ensure that the siRNA being used is not binding to other
related sequences.
Small-molecule inhibitors
There are many small-molecule inhibitors that target active sites on proteins. However,
there is a substantially smaller number of molecules that can target protein-protein interfaces and
disrupt the interaction, but some small-molecule inhibitors of bZIPs have been identified.
One of the first molecules shown to disrupt bZIPs binding to DNA was discovered using
a fluorescence anisotropy screen followed by multiple assays to ensure the selected compounds
34
bound specifically to the target (Figure 1.5a). After further analysis, it was shown that the
selected molecules did not disrupt the coiled-coil interaction, but instead bound to the basic
region of the bZIP dimer and further stabilized it, preventing the dimer from binding to DNA
(Rishi et al. 2005). When the bZIP dimer is not bound to DNA, the basic region is unstructured
(O’Neil et al. 1991); but binding of the small molecule to the basic region was shown to induce
structure and further stabilize the dimer. In a gel-shift assay, where binding of a protein to DNA
can be assayed, one of the selected compounds was able to inhibit the bZIP C/EBPα from
binding to DNA at 100 nM and was about 5-fold selective, as 500 nM was necessary to abolish
binding of the related bZIP C/EBPβ to DNA (Rishi et al. 2005). A later follow-up study using
similar compounds and derivatives of the selected compound found an inhibitor of the CREB
homodimer binding to DNA with an EC50 from a gel-shift assay of ~100 nM (Rishi et al. 2010).
Another small-molecule screen identified different inhibitors of ΔFOSB, a protein related
to FOSB that lacks the transactivation domain. Two compounds selected in the screen were
shown to inhibit ΔFOSB homodimers and ΔFOSB-JUND heterodimers from binding to DNA
(Figure 1.5b). One of the compounds, C2, worked similarly to the C/EBPα inhibitor discussed
above by binding to the unstructured basic region of the ΔFOSB homodimer and preventing it
from binding to DNA. The other C6 inhibitor was shown to bind to a region of the homodimer
likely N-terminal to the basic region and allosterically disrupt ΔFOSB binding to DNA.
However, under high-salt conditions that favor sequence-specific DNA binding, C2 was 10-fold
less effective. Compound C6 maintained effectiveness under physiological salt conditions, but it
was toxic to cells (Wang et al. 2012).
Three of the molecules identified up to this point bind to the basic region or very near it
and stabilize the dimer, preventing it from binding to DNA. However, none of the molecules
35
were tested against a large panel of bZIPs to ensure specific binding to the basic region of the
bZIP target protein. The basic regions of bZIPs are highly conserved (Amoutzias et al. 2007), so
it is possible the molecules may not be entirely specific for the given target. A better way to
ensure specificity would be to target the coiled-coil domain, and other groups have been using
different methods to engineer proteins and peptides that can specifically bind to a given bZIP
target.
Rational design of dominant-negative bZIP mutants
To selectively bind a particular bZIP target, the Vinson group took advantage of the
specificity encoded within the leucine-zipper domains. Krylov et al. designed an acid tag
composed of negative charges meant to complement the basic region (Krylov et al. 1995). When
not bound to DNA, a bZIP dimer is formed by the coiled-coil interaction, but the basic regions
are unstructured and destabilize the interaction due to the repulsion of the positive charges
(O’Neil et al. 1991). By replacing one of the basic regions in the dimer with the acid tag, a much
tighter complex is formed (Figure 1.5c). For example, by adding the acid tag to the N-terminus
of the FOS leucine zipper (A-FOS), the equilibrium dissociation constant of the FOS-JUN
interaction was tightened 3000-fold, and this stabilization was dependent on the interaction
between the leucine-zipper domains. Transfection of A-FOS into human hepatoma cells inhibits
JUN transactivation of genes, whereas transfection with the FOS bZIP domain or just the FOS
coiled coil does not inhibit transactivation (Olive et al. 1997). Other acid-tagged zippers have
been created targeting the bZIPs C/EBP (Krylov et al. 1995), CREB (Ahn et al. 1998), and
BZLF1 (Chen et al. 2011).
36
Figure 1.5
(a)
(b)
(c)
Figure 1.5 Structures of different bZIP inhibitors. (a) Small-molecule inhibitor of C/EBPα that binds to the basic
region and is five-fold specific for C/EBPα over C/EBPβ (Rishi et al. 2005). (b) Two small molecules targeting
ΔFOSB homodimers and ΔFOSB-JUND heterodimers (Wang et al. 2012). (c) Cartoon of A-ZIP inhibitors of bZIPDNA binding function (Krylov et al. 1995).
Addition of this acid tag to leucine zippers has not only created potent inhibitors but has
also revealed information about bZIP involvement in regulatory processes in mouse models. A
transgenic mouse expressing an acid-tagged zipper targeting bZIPs in both the C/EBP and AP-1
families revealed the roles of these proteins in white and brown fat expression, as these mice lack
white fat and are severely depleted in brown fat. These mice have become an established model
for studying diabetes induced by lack of fat (Moitra et al. 1998). Follow-up studies using this
mouse line have provided insight into the role of leptin in regulating bone mass (Elefteriou et al.
37
2004) and into adipose tissue developmental pathways (Rodeheffer et al. 2008). However, design
and use of A-ZIPs as reagents to dissect bZIP function can be complicated by the fact that many
bZIPs have multiple bZIP partners (Reinke et al. 2013). For example, A-FOS was designed to
inhibit JUN function, but it maintains the other native interactions the FOS bZIP makes. These
off-target bZIP interactions are also possible using other dominant-negative mutants of bZIPs.
The mutants lacking the transactivation regions of ATF5 and JUN used to show that inhibition of
ATF5 induces apoptosis in HeLa cells and inhibition of JUN increases the survival rate of
neuronal cells (Persengiev et al. 2002 and Ham et al. 1995), respectively, also maintain the
native binding partners of ATF5 and JUN. Therefore, analyses of transfection studies using bZIP
dominant-negative mutants may be difficult due to inhibition of multiple native bZIPs within the
cell.
Library selection of designed coiled coils
Another approach to identifying inhibitors of bZIP dimerization involves engineering
new proteins with desired specificities. Arndt and colleagues did much of the work targeting
particular bZIP proteins using protein libraries and a protein complementation assay (PCA). In
this method, library members are fused to one half of the enzyme murine dihydrofolate reductase
(mDHFR) and the target protein is fused to the other half. DHFR is necessary for E. coli survival
in minimal media in the presence of trimethoprim, which inhibits the prokaryotic DHFR. For
successful library members that bind the target protein, mDHFR is reconstituted and the cell
survives (Arndt et al. 2000). Libraries targeting both JUN and FOS were generated using
variations in core a, d, e, and g residues, and thermal stabilities of selected members with the
target protein were compared to the wild-type FOS-JUN stability. For the selected peptide
38
targeting JUN, the thermal stability of the complex increased by 47 °C compared to wildtype.
For the selected peptide targeting FOS, the thermal stability increased by 28 °C. However, this
FOS-binding peptide interacted more stably with JUN. Compared to its interaction with FOS, the
thermal stability of the selected peptide with JUN was 13 °C greater (Mason et al. 2006).
In a follow-up study, both the affinity and specificity for the target bZIP protein were
selected for in the presence of a competing bZIP protein (Figure 1.6a). In this version of the
assay, a peptide selected for binding to the target FOS is selected for in the presence of a JUN
construct that lacks the mDHFR fragment; therefore, any library members that bind
preferentially to JUN over FOS will not be selected. By incorporating the competing JUN into
the PCA, peptides selected for binding to FOS were shown to interact more stably with the target
FOS than with JUN, with a difference in thermal stability of 21 °C (Mason et al. 2007).
Incorporating a competing binding partner was helpful for selecting both tight and
specific binders of FOS. However, the selection is limited in the number of competing off-target
bZIPs that can be incorporated because all off-target proteins need to be expressed on external
plasmids. To consider many competing states, it is useful to turn to computational design, which
relies on predicted scores to assess whether two coiled coils will interact.
Computational design of coiled coils
Computational protein design relies on a function that can compute an energy “score”
indicating whether two proteins will interact, given their sequences as input. Because of the vast
amount of information known about how coiled coils interact, different scoring functions can be
generated. For example, in a “simple” scoring function, the coupling energies from the Vinson
lab that specify the energetic contribution to coiled-coil stability of residue pairs in the core can
39
be summed up across all a-a’ or e-g’ positions to provide an interaction score (Krylov et al.
1994, Acharya et al. 2002, Acharya et al. 2006, and Grigoryan and Keating 2006). A more
complicated function can consider information about hydrophobic pairing of core residues,
electrostatic interactions between core residues, and the helical propensity of residues in the
sequences. This function has been used to predict the thermal stabilities of coiled-coil homo- and
heterodimers (Mason et al. 2006).
Figure 1.6
(a)
(b)
Figure 1.6 Different methods to engineer specific binding to a target bZIP. (a) Library selection using mDHFR
complementation. Off-target bZIPs are expressed on an external plasmid. Library members that are not specific for
the target over the expressed off-targets will not be selected. (b) Cartoon of predicted energies that can be used in
computational design of specific binders. The predicted score between the design peptide and the target is compared
to scores between the design and off-targets. Sequence A is predicted to be specific for the target over the specified
off-targets.
As alternatives to sequence-based scoring functions, structure-based scoring functions
have also been developed. In these types of functions, a sequence is threaded onto a structural
template and the energy of the structure can be computed by evaluating van der Waals
40
interactions and electrostatic interactions (Harbury et al. 1998). In one study, a structure-based
scoring function was supplemented with the Vinson coupling energies in an attempt to accurately
model all potential amino-acid interactions between core residues. This scoring function was
used to design sequences that were specific for each human family of bZIPs. By calculating
energy scores between the designed sequence and the target bZIP sequence in addition to the
designed sequence with other off-target bZIPs (Figure 1.6b), all competing bZIP off-targets were
considered and many of the designed peptides were shown to be specific for the given target
using a coiled-coil protein array (Grigoryan et al. 2009)
Experimental methods to measure coiled-coil interactions
In order to determine whether the molecules or proteins being tested bind to the target
bZIP and are specific for the target, experimental assays need to be able to quantify the affinity
and specificity of the interaction being tested. In the final section, I discuss a few common
methods used to experimentally measure coiled-coil interactions.
Circular Dichroism (CD) spectroscopy
CD spectroscopy is a valuable technique for studying secondary structure content in
proteins, conformational changes in proteins upon binding other factors, protein folding, and
stability of biopolymers (Kelly et al. 2005). For bZIPs, CD was used to determine that the basic
region folds upon addition of its specific DNA site (Weiss et al. 1990) and that binding and
folding are coupled processes for heterodimer formation (Thompson et al. 1993). Thermal
stabilities monitored by CD were used to measure coupling energies between residue pairs in the
a-a’ and e-g’ positions (Krylov et al. 1994, Acharya et al. 2002, and Acharya et al. 2006).
41
Many groups have used CD to study the interaction between an engineered protein and a
target bZIP (Mason et al. 2006, Mason et al. 2007, Grigoryan et al. 2009, and Chen et al. 2011).
However, it is limited in its applications. Typically, the signal of a mixture of proteins is
compared to the signal of the individual components to determine whether an interaction that
leads to formation of additional secondary structure is occurring. In such an assay, it is difficult
to detect a heterodimeric interaction when both of the individual components form strong
homodimers. Additionally, CD requires a high concentration of very pure protein, so using this
low-throughput technique to test for off-target interactions to measure specificity is not ideal.
Calorimetry
Isothermal titration calorimetry (ITC) directly measures the thermodynamic effects of
two interacting molecules. As one protein is titrated into a cell containing another protein, bonds
between the solvent and protein are broken as new bonds between the proteins form, resulting in
an overall change in the Gibbs energy of the system. The change in Gibbs energy can be broken
down into changes in enthalpy and entropy, and by fitting the data to an appropriate model, the
entropy and enthalpy can be estimated (Ladbury and Chowdhry 1996).
ITC was used to measure equilibrium dissociation constants of different variants of the
bZIP GCN4 (Steinmetz et al. 2007), to study potential mechanisms of how JUN binds to DNA
(Seldeen et al. 2008a), and to determine that heterodimerization of bZIPs is enthalpically driven
(Seldeen et al. 2008b). But, like CD, it is limited in its use. Each experiment requires high
concentrations of very pure protein; thus, measuring many interactions to determine specificity is
not ideal.
42
Electrophoresis mobility-shift assay (EMSA)
The EMSA is a very useful and fast method for detecting protein-nucleic acid
interactions. The assay detects differences in electrophoretic mobility of a free nucleic acid and a
nucleic acid bound to protein. Depending on how the assay is performed, the assay can vary in
sensitivity. When a radioisotope-labeled nucleic acid probe is used, very small amounts of
nucleic acid and protein are needed. However, for fluorescent and chemiluminescent probes,
higher concentrations of reagents are needed. One big disadvantage this assay has over CD and
ITC is that complexes are no longer at equilibrium during the electrophoresis step, so complexes
with fast off-rates are often not detected (Hellman and Fried 2007). Other disadvantages include
requiring knowledge of the DNA-binding site to perform an EMSA and the assay not being a
direct measure of the protein-protein interaction.
Applied to bZIPs, EMSAs have been used to study how well small-molecule and protein
inhibitors can inhibit bZIP dimers from to binding DNA (Rishi et al. 2005, Olive et al. 1997, and
Chen et al. 2011), the DNA-binding specificities of bZIP dimers (Izawa et al. 1993), and how
post-translational modifications of bZIPs affect DNA binding (Hung et al. 2001). However, the
assay’s limitations on quantifying interactions, and the inability to measure many interactions in
parallel, do not make it amenable to measure the specificity of interactions.
Coiled-coil arrays
One of the best assays used to date to measure specificity is the coiled-coil array. Two
large-scale studies using coiled-coil arrays have been performed (Newman and Keating 2003 and
Grigoryan et al. 2009). Coiled-coil domains of the bZIPs were adhered onto the surface of a glass
slide and probed with a fluorescently-labeled coiled coil to detect interactions. This assay has
43
many advantages over CD, ITC, and gel-shift assays. First, the assay requires very little protein
for both the probe and the molecules adhered to the slide. Second, the fact that little protein is
needed allows for testing many interactions in parallel under identical conditions to measure
specificity. And third, the interactions are measured at the domain level, so issues like
proteolysis and incorrect folding have less of an effect than on full-length proteins (Newman and
Keating 2003).
A coiled-coil array was used by Grigoryan et al. to measure the interactions of peptides
designed to be specific for each target bZIP family. This array confirmed that many of the
design-target interactions were specific, but the assay provided only qualitative information
regarding the binding affinities of the interactions. Because only one concentration of the probe
was used and the interactions were not at equilibrium, an equilibrium dissociation constant was
not determined. Instead, relative binding strengths were determined by comparing the strength of
the fluorescent signals for all interactions tested for each probe. Additionally, the assay was
performed in the presence of 1 M guanidine hydrochloride. If a potential use of an engineered
protein targeting a bZIP is as a reagent to learn more about bZIP function, it must be determined
whether the proteins can interact under native-like conditions (Grigoryan et al. 2009).
In this thesis, I expand upon the Grigoryan et al. study. In Chapter 2, I describe how I
used a solution-based, moderate-throughput FRET assay to measure how tightly and specifically
some of the original designed peptides bind to their target bZIPs under native-like conditions. I
further optimized some of the designed peptides for tight binding to the given bZIP target by
looking at the components of the coiled-coil interaction as modular units and taking advantage of
the biophysical properties of a coiled-coil dimer. I split the coiled-coil interaction into two parts,
the core residues and the surface residues, and redesigned the surface residues of the designed
44
peptide for tighter binding to a given bZIP target. By introducing residues at those positions that
favor helix formation, I stabilized the design-target complex and showed that surface residues do
influence the interactions a particular coiled coil can make. In Chapter 3, I describe how I
experimentally validated a new design protocol that used a novel sequence-based scoring
function derived from experimentally calculated equilibrium dissociation constants. Designed
sequences were generated by breaking the coiled coil into individual heptad units, with each
heptad in the designed peptide optimized to bind to the corresponding heptad in the target
protein. Using this new scoring function and design method, successful peptides were generated
targeting different bZIP proteins.
45
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54
Chapter 2
Increasing the affinity of selective bZIP-binding peptides
through surface residue redesign
This chapter has been submitted for publication.
Collaborator notes:
Aaron Reinke contributed all of the labeled bZIP proteins and provided technical
assistance and is one of the authors on the submitted paper.
55
Introduction
Basic leucine-zipper (bZIP) transcription factors are responsible for regulating many
important processes within the cell including proliferation (Angel and Karin 1991), the unfolded
protein response (Korenykh and Walter 2012), tissue differentiation (Reza and Yasuda 2004),
and the response to oxygen or amino-acid deprivation (Ameri and Harris 2008). Fifty-three
bZIPs have been identified in humans based on conservation of a basic DNA-binding motif
followed by a parallel coiled-coil dimerization motif (Vinson et al. 2002 and Krylov et al. 1994).
The function of a bZIP protein depends on coiled coil-mediated dimerization to form a homo- or
heterodimer that can bind to DNA. The roles of individual bZIPs or bZIP pairs in various
biological processes have been studied for more than 25 years, yet a detailed global
understanding of how this family regulates gene expression, and how formation of competing
dimer complexes contributes to this process, is lacking.
In vitro studies measuring pair-wise interactions between all human bZIPs showed that
bZIPs do not indiscriminately dimerize with each other (Newman and Keating 2003 and Reinke
et al. 2013). Instead, the interactions made by bZIPs are highly specific, with some interactions
three orders of magnitude stronger than others (Reinke et al. 2013). This specificity is encoded in
the leucine-zipper coiled-coil interaction motif. At the sequence level, the coiled-coil domain is
characterized by heptad repeats, denoted (abcdefg)n, where a and d positions are usually
occupied by hydrophobic residues, with leucine very common at d. The e and g positions are
frequently occupied by long, charged or polar residues such as glutamate, glutamine, lysine or
arginine. At the structural level, hydrophobic residues form the central part of the interface of
the coiled coil, and residues at e and g positions can participate in electrostatic interactions across
the dimer interface. Residues at the surface b, c, and f positions point away from the coiled-coil
interface (Figure 2.1) (Krylov et al. 1994).
Much is known about how bZIP coiled-coil dimers interact in vitro. Folding studies on the
model bZIP GCN4 indicated that binding and folding are coupled processes in the coiled-coil
dimer (Thompson et al. 1993). This coupling equates the stability of helical structure with
56
interaction affinity. To relate sequence to interaction affinity, many researchers have carried out
mutational analyses of GCN4 and other coiled coils (Zhou et al. 1992, Vinson et al. 1993,
O’Shea et al. 1993, Hu et al. 1993, Zhou et al. 1994, Zhu et al. 2000). These studies culminated
in a large set of double-mutant cycle experiments performed by the Vinson group. Krylov et al.
and Acharya et al. quantified the energetic contributions to dimer stability of many residue pairs
in positions e-g’ and a-a’, where a prime indicates a position on the opposite coiled-coil chain
(Krylov et al. 1994, Acharya et al. 2002, and Acharya et al. 2006). The large amount of in vitro
data describing how coiled coils interact has led to the development of peptides that can bind to
native bZIP proteins and inhibit their function. However, a particular challenge has been to
identify peptides that are selective for targets of interest, given the many families of related
human bZIPs.
Figure 2.1
Figure 2.1 Helical-wheel diagram of a parallel 2-helix coiled coil. The core residues are shown in black squares
and boxed in a dashed black line, the surface residues are in gray circles and boxed in a solid line. The dashed gray
arrows, between residues at e and g’ and g and e’, show common sites for electrostatic interactions.
Previous work designing peptide binders to target bZIP proteins used library selection
techniques (Mason et al. 2006, Mason et al. 2007, and Mason et al. 2009), rational design (Olive
et al. 1997, Ahn et al. 1998, and Arndt et al. 2002), or computational design (Grigoryan et al.
2009, Reinke et al. 2010, and Chen et al. 2011). Mason et al. performed a library selection in
57
engineered bacteria using a protein complementation assay that involved fusing library members
to one fragment of the murine enzyme dihydrofolate reductase (mDHFR) and a target coiled-coil
domain - from either FOS or JUN - to the other fragment (Mason et al. 2006). Thermal stability
analysis established that a peptide selected to target FOS using this assay bound more stably to
JUN than to FOS. Therefore, in a follow-up study, the JUN peptide was introduced on a separate
plasmid as a competing off-target. This modification of the assay made it possible to select for
both stable and specific binders, and thermal stability analysis confirmed the successful selection
of peptides specific for binding to FOS in preference to JUN (Mason et al. 2007b).
In a series of papers, Vinson and colleagues demonstrated a strategy to use the specificity
encoded in native bZIP proteins to engineer bZIP inhibitors. Their approach involved replacing
the basic DNA-binding sequence of a native binding partner with an acidic extension that would
provide enhanced affinity, via interaction with the basic region of a partner. Thus, to inhibit JUN
function, an acidic sequence was fused to the FOS coiled coil, and the resulting ACID-FOS, or
A-FOS, bound 3000-fold more tightly to JUN than the native FOS bZIP (Olive et al. 1998). AZIPs have been generated and used to target the bZIPs CEBP (Krylov et al. 1995), CREB (Ahn
et al. 1998), and BZLF1 (Chen et al. 2011). The engineered peptides provided insights into the
role of AP-1 in recruiting the glucocorticoid receptor to specific DNA sequences (Biddie et al.
2011) and have been used in a transgenic mouse line to study the role of white fat in diabetes
(Moitra et al. 1998).
Previously designed bZIP-binding reagents were demonstrated to be selective for their
targets over only a small number of other bZIPs. Global specificity with respect to all human
bZIPs was not tested, but could be difficult to engineer using these approaches. In the DHFR
complementation assay, specificity is limited by the number of plasmids encoding competing
targets that can be included in an experiment. In the A-ZIP approach, dimerization specificity is
determined by the limited repertoire of native bZIP coiled coils. E.g., A-FOS can bind to JUN
but also to all other FOS-binding bZIP proteins. To design a protein capable of selectively
binding to a particular target, in preference to dozens of other possible bZIP proteins, Grigoryan
58
et al. used computational design to model large numbers of competing states as described below
(Grigoryan et al. 2009).
A variety of computational methods can be used to estimate how well two proteins will
interact, given a model structure or a pair of sequences. In a physical, structure-based analysis,
van der Waals interactions, electrostatics, and solvation effects are calculated from atomic
coordinates (Ponder and Case 2003 and Eisenberg and McLachlan 1986). In a statistics-based
approach, a score for an interaction is derived from the distribution of either atom-atom or
residue-residue interactions in known protein structures (Zhou and Zhou 2002 and Lu et al.
2008). A third approach uses data-driven models. The coupling energies measured by Vinson
and colleagues provide a simple data-driven model in which scores are obtained by summing the
contributions of individual residue-residue interactions, for cases where these have been
measured. Alternatively, models derived empirically or trained by machine learning, using
literature reports of coiled-coil interactions, can provide artificial weights for scoring residueresidue interactions (Mason et al. 2006 and Fong et al. 2004). Using a large set of experimental
bZIP interaction data, Grigoryan and Keating showed that purely structure-based energy
functions did not perform as well at predicting interactions as functions supplemented with
experimental or machine-learned weights that estimated the interactions between certain core
positions (Grigoryan and Keating 2006).
Grigoryan et al. went on to use the best identified scoring functions to design bZIP-binding
peptides. “Anti-bZIP” peptide sequences were generated in a two-step process that considered
both interaction with the desired target and potential interactions with numerous other off-target
bZIP proteins (Grigoryan et al. 2009). Because the core positions were judged to be more
important for interaction affinity and specificity (O’Shea et al. 1992 and O’Shea et al. 1993), and
because the computational models used only these sites to predict interactions, positions a, d, e,
and g were designed first. In a second step, the surface residues b, c, and f were selected to
complement the core residues, based on residues found at these sites in native bZIP coiled coils.
Designed sequences were experimentally tested with a coiled-coil array assay for
59
interaction with the target, representatives of other bZIP families, and for homodimerization.
There were many successes. Many designed peptides showed evidence of preferential binding to
the intended target bZIP protein and/or a closely related family member on the array, and
selected candidate interactions were validated in solution using circular dichroism (CD)
spectroscopy. Two of the best designs targeted CREBZF and FOS. Anti-CREBZF and anti-FOS
were shown to bind strongly and selectively to their target bZIPs on the array. However, in other
cases the array signal for the intended design-target interaction was low, relative to other
interactions made by the target. This was true for anti-XBP1 binding to XBP1. For anti-ATF6
binding to ATF6, the relative stability of the design-target complex was moderate, and antiATF6 bound better to XBP1 than to ATF6 (Grigoryan et al. 2009). For the XBP1 and ATF6
examples, the array data suggest that a weak complex was formed.
Designing specific anti-bZIPs is difficult due to the trade-off between specificity and
stability in protein design (Ali et al. 2005, Baker et al. 2005, Grigoryan et al. 2009, and Fromer
and Shifman 2009). When designing a protein for selective interaction with one particular target,
affinity of the desired interaction is often sacrificed in order to eliminate undesired off-target
interactions. A second version of the anti-XBP1 peptide that was designed to bind more tightly to
XBP1 did show a stronger interaction, but this peptide was not as specific as the original peptide.
However, for targeting bZIP proteins, the design of both tight-binding and specific peptides is
important, so determining ways to optimize both specificity and affinity would be beneficial.
One strategy to improve the binding affinity of the anti-bZIP peptides would be to fuse an
acidic extension to the N-terminus of the design sequence (Krylov et al. 1995). Chen et al. used
this approach to improve the affinity of a peptide targeting the viral bZIP protein BZLF1 (Chen
et al. 2011). However, including an acidic extension increases both the length and the overall
negative charge of the engineered molecule, which is undesirable because small, positively
charged peptides have a better chance of crossing the cell membrane (Bernal et al. 2007).
Furthermore, the A-ZIP strategy is limited to targeting bZIP proteins, and more general
60
techniques for stabilizing designed coiled-coil peptides could find broad application due to the
high frequency of coiled coils in the human proteome (Rose et al. 2005).
A potentially general strategy for optimizing the affinity of coiled-coil interactions is to
take advantage of the coiled-coil structure and biophysical properties of the dimer. Due to the
nature of the coiled-coil dimer, which has core residues pointing inward and surface residues
pointing outward into solution, one design strategy could be to optimize the coiled-coil core
residues for specificity and affinity, because they form the most critical inter-helical interactions
(O’Shea et al. 1992), and then optimize the surface residues to further improve design-target
affinity. One way to improve stability would be to globally stabilize designed complexes by
increasing their helix propensities. Prior redesign of the surface residues of GCN4 to introduce
stabilizing hydrogen bond interactions and increase helical propensity created more thermally
stable mutants relative to wild type (Dahiyat et al. 1997). Additionally, Zitzewitz et al.
previously demonstrated that the stability of GCN4 was increased by 1.2 - 1.3 kcal/mol when
alanine or glutamine were substituted at four f positions (Zitzewitz et al. 2000). This strategy of
stabilizing the coiled coil is analogous to the A-ZIP approach in that it could potentially stabilize
all interactions, but it can potentially be applied generally to many coiled coils.
In this study, we first used a solution FRET assay to quantify the affinities between four
previously designed peptides, anti-FOS, anti-CREBZF, anti-XBP1, and anti-ATF6, and their
targets. These values were compared to the dissociation constants between the designed peptides
and 31 off-targets, including the designed peptide homodimer and 30 other human bZIP proteins
spanning multiple bZIP families, to quantify binding specificity. We then used the idea of coresurface modularity to redesign the surface residues of some of these peptides to test whether
incorporating residues that favor helix formation would stabilize the design-target interaction.
Results
Solution characterization of original designed anti-bZIP peptides
61
All of the FRET binding studies done with anti-bZIP peptides in this work, and by
Grigoryan et al., used constructs including only the DNA-binding and coiled-coil dimerization
regions of the human proteins (Grigoryan et al. 2009). The analytical ultracentrifugation
experiments used only the coiled-coil domain of the target protein (see Methods). The coiled-coil
array initially used to study anti-bZIP peptides was useful in that it allowed many interactions to
be tested in parallel under identical experimental conditions using a small amount of protein. The
assay gave good qualitative agreement with literature reports and solution stability studies
(Newman and Keating 2003 and Grigoryan et al. 2009), and subsequent quantitative binding
studies done in solution support the utility of the assay for classifying strong versus non/weak
interactions (Reinke et al. 2013). However, the arrays do not provide equilibrium dissociation
constants. Additionally, the array assay was performed in non-native, high-salt conditions (1 M
guanidinium chloride). Many interactions between coiled coils involve the formations of salt
bridges between core positions (Mason et al. 2009 and Krylov et al. 1998), and high-salt
conditions could weaken those interactions. Krylov et al. showed that the coupling energy
between glutamate and arginine or lysine at e and g’ positions was reduced from -0.6/-0.5
kcal/mol per interaction at 5 mM KCl, respectively, to -0.3/-0.1 kcal/mol at 1.5 M KCl (Krylov
et al. 1998). Therefore, to better assess how successful the design process was, we used a
previously described fluorescence resonance energy transfer (FRET) assay to measure
equilibrium dissociation constants between fluorophore-labeled molecules at a more
physiologically relevant salt concentration of 150 mM KCl (Reinke et al. 2013) (see Methods).
For the FRET assay, we attached a Rhodamine Red FRET acceptor dye to the anti-bZIP
peptide via a unique C-terminal cysteine residue (Table 2.1). The acceptor-labeled designed
peptide was titrated against a panel of fluorescein-labeled human bZIP proteins, and an
equilibrium dissociation constant was fit for each interaction tested, as described in the Methods.
We then compared the dissociation constant for the desired design-target interaction with the 31
design-off-target interactions to quantify how specific the design was. We monitored interactions
at 4, 23, and 37 °C and used the low-temperature data to detect weaker interactions.
62
All dissociation constants are given in Tables 2.5-2.10, after the Discussion. At 37 °C, the
dissociation constant between anti-FOS and FOS was < 1 nM; between anti-CREBZF and
CREBZF it was 1.4 nM; between anti-ATF6 and ATF6 it was 103 nM; and between anti-XBP1
and XBP1 it was not detectable up to 1 µM of peptide (Figure 2.2 and Table 2.2). To analyze the
binding specificity of each design, the dissociation constant of the desired interaction was
compared to the strongest interaction with any off-target protein in a different bZIP family
(Grigoryan et al. 2009). Results were mostly consistent with the conclusions from the array
study. Anti-CREBZF was about 25-fold specific for CREBZF over its next best interacting
partner CEBPG, with which CREBZF shares 34.7% sequence identity in the core residues. AntiATF6 was about 3-fold specific for ATF6 over its next best non-related interacting partner XBP1
(ATF6 and XBP1 share 49% core sequence identity). However, anti-XBP1 did not bind
detectably to XBP1, except at 4 ºC, at which temperature the dissociation constant was 415 nM.
Stronger interactions were detected between anti-XBP1 and several other targets at all
temperatures tested (Figure 2.2 and Tables 2.6 and 2.7 after the Discussion).
Table 2.1
Table 2.1 Sequences of designed peptides and bZIPs targeted. Residues in red differ between the original
designed sequence and the surface-redesigned sequence. Residues underlined in OPTanti-XBP1_A and OPTantiXBP1_B differ from each other.
The interaction anti-FOS made with FOS was too tight to accurately quantify under the
assay conditions, so the interactions of anti-FOS with FOS and with tight off-target partners
63
CEBPG, BATF3, and CREB3L3 were re-measured in 3 M urea. Under those conditions, the
dissociation constant at 37 °C for the interaction between FOS and anti-FOS was 30 nM. No
binding was detected with the other bZIPs up to 1 µM of acceptor, indicating that the design was
specific for FOS under these conditions (Figure 2.2 and Table 2.2).
Table 2.2
Table 2.2 Measured affinities between designed peptides and bZIP targets. The measured affinity between the
designed peptide and bZIP target indicated is shown, along with the strongest off-target interaction. 1Values in
parentheses give the affinity in 3 M urea. 2Affinity with the related family member ATF6B is given in parentheses.
Figure 2.2
Figure 2.2 Specificity profiles of anti-FOS, anti-CREBZF, anti-ATF6, and anti-XBP1 at 37 °C. Sequence
identities between the target and other bZIPs are indicated in greyscale and were calculated using only core residues.
All Kd values and bZIP names are listed in Table 2.5 after the Discussion. Target siblings are proteins in the same
bZIP family as the target, with families defined as in (Grigoryan et al. 2009).
64
Out of 98 interactions tested in both assays, there was 76% agreement with respect to
bZIP pairs interacting vs. not interacting in the array assay versus the FRET assay (see Methods).
This is somewhat lower than the agreement determined previously for a different set of coiledcoil interactions (Reinke et al. 2013). Ten percent of the interactions tested appeared only on the
array, and 14% were only detectable by FRET. Several differences between the assays could
explain these observations. For example, fifty percent (5/10) of the interactions detected on the
array but not with the FRET assay involved a designed peptide competing with a bZIP
homodimer that had a dissociation constant less than 25 nM. On the arrays, human bZIP peptides
were attached to the surface of the glass in the presence of 6 M guanidine hydrochloride, in an
attempt to display them as monomers for binding. In the solution FRET assay, however,
designed peptides had to compete with target homodimers, making the interactions more difficult
to detect (Reinke et al. 2013). The 14% of interactions detected only by FRET may be due to the
differences in the assay conditions, e.g. to the presence of 1 M denaturant in the array assay
buffer. However, there are additional reasons that the solution FRET assay may be more
sensitive, as was previously observed (Reinke et al. 2013). In the FRET assay, each designed
peptide was labeled on a unique terminal cysteine residue, and the human bZIPs were prepared
using intein chemistry to label a specific lysine at the carboxy terminus. This strategy was chosen
to minimize the influence of labeling on the interaction. For the array experiments, both peptide
surface attachment and fluorophore labeling occurred much less specifically on primary amines,
presumably including the N-terminal amine and lysine residues. bZIPs have lysine residues in
core positions that contribute to key interactions (Krylov et al. 1994), and blocking these by
fluorophore or surface attachment could interfere with binding. As a superior indicator of the
expected behavior when a peptide is used as an inhibitor in solution under physiological
conditions, we favor the FRET assay.
Based on the solution measurements reported here, anti-CREBZF was determined to be a
highly successful design. We analyzed this design:target complex as a 1:1 mixture using
analytical equilibrium ultracentrifugation (see Methods) and determined that the complex had the
65
molecular weight expected for a heterodimer (Table 2.3). Anti-CREBZF was not studied further
in this work.
Table 2.3
Table 2.3 Fitted masses of design-target complexes from analytical ultracentrifugation. 1 RMSD describes the
deviation between the experimental data and the fit. The fits reported are for mixtures of 20 µM design peptide + 20
µM coiled-coil target.
Design and testing of surface-redesigned anti-bZIP peptides
We chose anti-XBP1, anti-ATF6, and anti-FOS for our surface redesign study. Surface
residues were originally chosen using a constrained optimization procedure. The objective was to
maximize the conditional probability of designed b, c, and f residues, given previously designed
core residues, based on the frequencies of residue pairs in >400 native bZIPs (Grigoryan et al.
2009). Constraints were placed on the peptide net charge, total number of charged residues, and
total helix propensity to ensure the final sequence had properties similar to native bZIP coiledcoil sequences. To increase the affinity of the design-target interaction, we took advantage of the
fact that binding and folding in coiled coils are coupled processes. Previous studies showed that
increasing helical content in the GCN4 dimer by incorporating residues with high helix
propensity stabilized the dimer relative to wild type (Dahiyat et al. 1997 and Zitzewitz et al.
2000). We incorporated this information into the design process by adding two new constraints
on potential salt-bridge formation between surface residues at i-i+3 and i-i+4 positions, and
relaxing the constraints on total helix propensity and charged residues, to design sequences that
would favor helix formation (see Methods). The final redesigned sequences incorporated more
residues with high helical propensities and the potential to form intra-helical salt bridges (O’Neil
66
and DeGrado 1990 and Munoz and Serrano 1994). All of the final sequences were more highly
charged than the original sequences (Table 2.4).
Peptides designed to bind to XBP1
For XBP1, we chose two re-designed sequences for experimental testing, OPTantiXBP1_A and OPTanti-XBP1_B. OPTanti-XBP1_A differed from the original design at seven
residues and OPTanti-XBP1_B differed at eight, and these two new peptides differed at five
residues from each other (Table 2.1). The original anti-XBP1 peptide had a net charge of +1, but
OPTanti-XBP1_A and OPTanti-XBP1_B had net charges of +10 and +7, respectively. The
predicted helical propensities for both re-designed peptides were greater than the original
designed peptide based on the helix propensity scale in (O’Neil and DeGrado 1990), and both
were predicted by AGADIR to be more helical in solution under the conditions used in the FRET
assay (Table 2.4) (Lacroix et al. 1998). The magnitude of the helical propensity predicted for
OPTanti-XBP1_A was slightly greater than for OPTanti-XBP1_B, but OPTanti-XBP1_A was
predicted to be less helical by AGADIR because OPTanti-XBP1_B had more favorable predicted
energies between residues in i-i +3 and i-i+4 positions (Lacroix et al. 1998).
Table 2.4
Table 2.4 Sequence properties of designed peptides. 1 HP = Helical propensity. Helical propensities were
calculated using the scale in O’Neil and DeGrado 1990. 2 AGADIR helical predictions were made using the
conditions for the FRET assay (37 °C, 150 mM KCl, pH 7.4). 3Net charge and number of charged residues were
calculated assigning +1 to arginine and lysine residues and -1 to glutamate and aspartate residues.
67
We measured the CD spectra of the original anti-XBP1 design, OPTanti-XBP1_A, and
OPTanti-XBP1_B. At a concentration of 10 µM at 25 °C, designs OPTanti-XBP1_A and
OPTanti-XBP1_B showed evidence of partial helical structure, with a weak minimum in each
spectrum at 222 nm (Figure 2.3a). Using the Baldwin method (Scholtz et al. 1991), the original
anti-XBP1 design, OPTanti-XBP1_A, and OPTanti-XBP1_B were estimated to be 20%, 44%
and 38% helical. Neither anti-XBP1 nor OPTanti-XBP1_A exhibited a CD signal that was
concentration-dependent over the range of 10 to 30 µM, suggesting the observed helicity did not
arise from homodimerization of the designed peptide. OPTanti-XP1_B showed a reproducible
but unexplained small decrease in mean residue ellipticity at concentrations over 10 µM (Figures
2.3b and 2.3c).
Figure 2.3
(a)
(b)
(c)
Figure 2.3 CD spectra of the designed peptides anti-XBP1, OPTanti-XBP1_A, and OPTanti-XBP1_B. (a)
Scans were performed at 25 °C with 10 µM peptide. Helical content calculated by the Baldwin method (Scholtz et
al. 1990) was determined to be 20% for anti-XBP1, 44.3% for OPTanti-XBP1_A and 38.3% for OPTanti-XBP1_B.
(b) Scans of OPTanti-XBP1_B performed at 25 °C and (c) 37 °C.
68
We tested both OPTanti-XBP1_A and OPTanti-XBP1_B in the FRET assay for binding to
XBP1, and the dissociation constants were 56 nM and 181 nM, respectively, at 37 ºC (Table 2.2).
This was a large improvement in affinity from the original design-target interaction, as that
interaction was not detectable up to 1 µM at 37 ºC. Comparisons can be made at 4 ºC, where the
original anti-XBP1 interacted with XBP1 with an affinity of 415 nM. OPTanti-XBP1_A and
OPTanti-XBP1_B bound to XBP1 at 4 ºC with dissociation constants of 3 nM and 5.5 nM,
respectively, corresponding to increases in affinity of 138-fold and 75-fold over the original
design. These changes validate our strategy of introducing helix-stabilizing residues as a way to
improve binding affinity.
Measuring the specificity profiles for binding to 30 other human bZIPs revealed dramatic
differences in specificity for OPTanti-XBP1_A versus B (Figure 2.4a). The only interactions
detected with OPTanti-XBP1_A at 37 °C with a dissociation constant less than 1 µM were with
XBP1 and with the related bZIP ATF6B (46 % sequence identity for core residues), indicating a
highly successful re-design. Analytical ultracentrifugation confirmed that a 1:1 mixture of XBP1
and OPTanti-XBP1_A gave a complex with the molecular weight expected for a heterodimer
(Table 2.3). However, OPTanti-XBP1_B gained many new, strong interactions with unrelated
bZIP proteins, in addition to losing detectable binding to some of the partners of the original
anti-XBP1 peptide (Table 2.5, after the Discussion). A few of the interactions made by OPTantiXBP1_B were observed for OPTanti-XBP1_A at lower temperatures (Tables 2.6 and 2.7, after
the Discussion), but the changes in the specificity profile could not be accounted for by a global
stabilization. Sixty-four total interactions were tested for OPTanti-XBP1_A and OPTantiXBP1_B, of which 33 gave evidence for binding at 23 °C. Interactions assigned Kd < 1000 for
both designs are plotted in Figure 2.4b.
69
Figure 2.4
(a)
(b)
(c)
Figure 2.4 Characterization of OPTanti-XBP1_A and OPTanti-XBP1_B by FRET. (a) Specificity profiles of
OPTanti-XBP1_A and OPTanti-XBP1_B at 37 °C. All Kd values are listed in Table 2.5 after the Discussion. (b)
Comparison of affinities (in M) of OPTanti-XBP1_A and OPTanti-XBP1_B targets at 150 mM KCl at 23 °C. All Kd
values are listed in Table 2.6 after the Discussion. (c) Comparison of affinities (in M) of OPTanti-XBP1_A and
OPTanti-XBP1_B targets at 400 mM KCl at 23 °C. All Kd values are listed in Tables 2.8 and 2.9 after the
Discussion.
One property that differed between designs A and B was the total number of charged
residues. The sequence changes from OPTanti-XBP1_A to B decreased the formal charge from
+10 to +7 (counting Asp and Glu as -1 and Lys and Arg as +1), with fewer negative charges in
OPTanti-XBP1_A contributing to a reduction in electrostatically favorable residue pairings at ii+3 and i-i+4 positions. However, OPTanti-XBP1_B had more charged residues than the other
70
two designs (Table 2.4). To assess the role of electrostatics in modulating specificity, we remeasured the interactions that differed significantly between OPTanti-XBP1_A and B in a buffer
with a higher salt concentration.
Moderately high concentrations of salt weaken electrostatic interactions between side
chains (Collins 1997). Under these conditions at 23 °C, the affinities of all of the interactions
made by both OPTanti-XBP1_A and OPTanti-XBP1_B were within two-fold of each other, with
the target XBP1 interaction the tightest interaction detected, with Kd ~10 nM (Figure 2.4c and
Tables 2.8 and 2.9, after the Discussion). Dissociation constants for just three of the OPTantiXBP1_A interactions changed by more than 3-fold between the two salt conditions: interactions
between OPTanti-XBP1_A and CREBZF, JUNB, and BACH2 became tighter. Changes in the
OPTanti-XBP1_B peptide binding profile were more dramatic; all dissociation constants
changed by at least 3-fold. All measured OPTanti-XBP1_B interactions became weaker, except
interactions with XBP1, FOS, and ATF6B. These data suggest that the large number of charged
residues in OPTanti-XBP1_B had a significant electrostatic influence on the specificity profile.
To better understand which charges were important for the high binding promiscuity of
OPTanti-XBP1_B compared to OPTanti-XBP1_A, we made a mutant version of this peptide.
Only five residues differ between OPTanti-XBP1_A and B (Table 2.1), four of which correspond
to glutamates in OPTanti-XBP1_B at positions where OPTanti-XBP1_A has a neutral residue.
We mutated these four glutamic acid residues to glutamine, to give OPTanti-XBP1_B-GLN.
This peptide was tested in the FRET assay against the eleven bZIP proteins that showed a large
difference in affinity for OPTanti-XBP1_A versus B. Of the eleven interactions tested, the
interactions with XBP1, FOS, and ATF6B were stronger with OPTanti-XBP1_B-GLN than with
OPTanti-XBP1_B at 23 °C under assay conditions using 150 mM KCl. These are the same
71
interactions that were stronger with OPTanti-XBP1_B in 400 mM salt. The other eight
interactions tested all became weaker (Table 2.10 after the Discussion). Mutating the glutamic
acid residues to glutamine thus had the same effect on interactions made by OPTanti-XBP1_B as
testing OPTanti-XBP1_B in high-salt conditions. Comparison of the interactions made by
OPTanti-XBP1_B-GLN in 150 mM salt to those made by OPTanti-XBP1_B in 400 mM salt
shows how the specificity profiles converged (Figure 2.5a). Eight of the interaction affinities
were within 2-fold, and three were within 3-fold of each other. Additionally, all of the
interactions made by OPTanti-XBP1_B-GLN, other than the interaction with JUNB, were within
2-fold of the affinity of the interactions made by OPTanti-XBP1_A in 150 mM salt (Figure
2.5b).
Figure 2.5
(a)
(b)
Figure 2.5 Characterization of OPTanti-XBP1_B-GLN. (a) Comparison of affinities (in M) of OPTantiXBP1_B-GLN at 150 mM KCl with OPTanti-XBP1_B at 400 mM KCl at 23 °C. All Kd values are listed in Tables
2.9 and 2.10 after the DIscussion. (b) Comparison of affinities (in M) of OPTanti-XBP1_A and OPTanti-XBP1_BGLN for targets at 150 mM KCl at 23 °C. All Kd values are listed in Tables 2.8 and 2.10 after the DIscussion.
The net charges of the human bZIP peptides are consistent with long-range electrostatics
contributing to some of the differences in specificity profiles between OPTanti-XBP1_A (net
72
charge +10) and OPTanti-XBP1_B (net charge +7). For example, out of 11 bZIPs that interacted
differently with OPTanti-XBP1_A and OPTanti-XBP1_B, the two most positively charged
(NFE2L1 and JUNB) interacted more tightly with OPTanti-XBP1_B, and the two most
negatively charged (FOS and XBP1) interacted more tightly with OPTanti-XBP1_A (Figure
2.6). Yet there were several examples of bZIPs with formal negative charges that interacted more
tightly with OPTanti-XBP1_B than OPTanti-XBP1_A (e.g. BACH1, BACH2, NFE2, MAFG).
Of course, formal charge estimated by residue composition is only a very rough indication of the
charge state of a molecule, and specific interactions may be more important than overall charge
state.
Figure 2.6
Figure 2.6 Net charges of bZIPs used in the study. A histogram showing the distribution of bZIP leucine-zipper
domains having a particular net charge. Underlined bZIPs interacted more tightly with OPTanti-XBP1_B than with
OPTanti-XBP1_A.
Peptides designed to bind to ATF6 and FOS
The experimental data indicated that introducing residues that promote helical content can
stabilize the design-target interaction. Therefore, we repeated the redesign process for anti-ATF6
and anti-FOS. In the optimized anti-ATF6 peptide (Table 2.1), the redesigned sequence differed
from the original sequence in six positions, four of which included a charged residue to facilitate
73
possible i, i+3 and i, i+4 side-chain interactions, and one of which removed a charged residue.
These changes resulted in a large difference in formal peptide charge of +7, compared to 0 for
the original design (Table 2.4). The optimized anti-FOS sequence differed from the original
sequence in five positions, giving an overall formal peptide charge of +11 compared to +6 for
the original design (Tables 2.1 and 2.4).
Analysis of the design-target interactions for the surface-redesigned peptides again showed
that the redesign process led to tighter binding. The optimized anti-ATF6, OPTanti-ATF6, bound
to ATF6 6-fold tighter than the original version, with a dissociation constant of 17 nM at 37 °C
(Figure 2.7a and Table 2.2). A mixture of ATF6 and OPTanti-ATF6 gave a complex with the
molecular weight expected for a heterodimer, based on analysis by analytical ultracentrifugation
(Table 2.3). The specificity profile of OPTanti-ATF6 was altered, although much less
dramatically than when anti-XBP1 was redesigned to give OPTanti-XBP1_B. OPTanti-ATF6
bound more weakly than anti-ATF6 to unrelated bZIPs, such as NFE2L1 and ATF4, but
interacted more tightly with target ATF6 family members and the related bZIP proteins XBP1
and CREBZF, which share similar sequence features with the ATF6 family. The tightest
interaction made by OPTanti-ATF6, with a dissociation constant of 6 nM, was with ATF6B, a
member of the ATF6 family. The two next tightest interactions were with XBP1, with a
dissociation constant of 10 nM, and the target ATF6, with a dissociation constant of 17 nM
(Table 2.5 after the Discussion). ATF6B and XBP1 share 67% and 50% sequence identity,
respectively, with the core residues of ATF6.
Anti-FOS already bound very tightly to FOS and our assay was not sensitive enough to
detect improvement in OPTanti-FOS binding at 37 °C. To determine whether surface redesign
improved binding, we assayed anti-FOS and OPTanti-FOS in 3 M urea and found that the
redesigned peptide bound 3-fold tighter than the original design under these conditions (Kd of 11
versus 30 nM at 37 °C) (Figure 2.7a and Table 2.2). The specificity profile of OPTanti-FOS was
74
tested, and close competing off-target interactions made by OPTanti-FOS were tested in 3 M
urea.
Figure 2.7
(a)
(b)
Figure 2.7. Characterization of OPTanti-ATF6 and OPTanti-FOS. (a) Specificity profiles of the surfaceredesigned OPTanti-ATF6 and OPTanti-FOS at 37 °C plotted as in Figure 2.2. (b) anti-FOS and redesigned
OPTanti-FOS acting as inhibitors of 10 nM FOS with 10 nM JUN binding to 40 nM DNA at 37 °C. The IC50 for
anti-FOS is 28.8 nM and for OPTanti-FOS it is 10.9 nM.
Under these conditions, the design-target interaction was the only binding event detected,
indicating OPTanti-FOS was still highly specific for FOS. In the absence of urea at 37 °C,
interactions with 11 bZIPs changed more than 3-fold between anti-FOS and OPTanti-FOS, but
75
only two of these interactions were weaker with the new design (Table 2.5 after the Discussion).
Two new interactions, with ATF2 and NFE2L2, were observed with OPTanti-FOS at 37 °C and
were not detected at any temperature tested with anti-FOS. One interaction, observed between
CREB3L3 and anti-FOS at 37 °C, was no longer detected with OPT-anti-FOS at any temperature
tested. Thus, although the affinities of many interactions changed between the redesigned and
original peptides, the total set of interaction partners did not change as dramatically as was
observed for the anti-XBP1 designed peptides.
Because anti-FOS and OPTanti-FOS bound very tightly to FOS, we tested whether these
peptides could inhibit FOS-JUN binding to DNA. FOS alone does not bind DNA (Rauscher et al.
1988). Instead, members of the FOS family bind DNA as heterodimers with members of the JUN
family, or other families (Mason et al. 2007a and Olive et al. 1997). To test for inhibition, we
used a competition FRET assay where an unlabeled designed peptide was mixed with FITClabeled FOS, TAMRA-labeled JUN, and DNA (Reinke et al. 2013). The FOS-JUN bZIP dimer
bound to a consensus AP1 DNA site forms a FRET complex. Figure 2.7b compares the two
designed anti-FOS peptides acting as inhibitors of FOS-JUN binding to DNA (Figure 2.8). Both
designed peptides were potent inhibitors, with IC50 values of 28.8 nM and 10.9 nM for anti-FOS
and OPTanti-FOS, respectively.
Discussion
In all cases tested here, surface redesign intended to increase peptide helicity led to
tighter binding of anti-bZIP peptides to their targets, as anticipated. However, changing the
surface residues also changed the specificity of the designed peptides in unpredicted ways. A
76
subtle coupling between core and surface residues led to dramatic changes in binding for the
highly charged OPTanti-XBP1_B peptide and smaller changes for three other designs.
Figure 2.8
(a)
(b)
Figure 2.8 Controls showing the peptides anti-FOS and OPTanti-FOS inhibit FOS-JUN binding to DNA. (a)
Lack of inhibition by anti-FOS and OPTanti-FOS of the JUN homodimer (20 nM total) bound to 40 nM of a
consensus AP1 site. (b) Inhibition of FOS-JUN (20 nM total) by anti-FOS and OPTanti-FOS. Lack of a lower
baseline and larger fluorescent signal detected at the lowest inhibitor concentrations compared to when DNA is
present (Figure 2.7b) suggests the complex being inhibited in Figure 2.7b is FOS-JUN bound to DNA.
In the redesign process, we attempted to increase the helicity of the designed peptides by
incorporating more residues with greater helix propensity and introducing more charged residues
at b, c and f positions that could potentially participate in stabilizing intra-helical electrostatic
interactions. However, incorporating charged residues into the surface positions changed the
specificity profiles of the designs. Evidence that an electrostatic mechanism was responsible for
altering the specificity of OPTanti-XBP1_B was provided by the convergence of specificity
profiles for OPTanti-XBP1_A and B when interactions were measured in higher salt conditions
and when charged glutamate residues were mutated to polar but neutral glutamines (Figures 2.4c
and 2.5b).
Numerous successful attempts to design coiled coils have focused on choosing the core a,
d, e and g residues while choosing b, c and f positions in a more ad hoc manner (Vinson et al.
1993, O’Shea et al. 1993, Zhou et al. 1994, and Havranek and Harbury 2002). However, from a
77
structural/biophysical perspective, it is not surprising that changes to residues on the surface of
designed helices, especially changes in charge, can perturb interactions. Differences in longrange electrostatics may alter design-target association, as discussed above. In addition, charges
at b, c and f positions can likely influence residues at e and g positions by making specific
contacts. Engineering e and g residues is an important strategy for controlling attractive versus
repulsive inter-helical charge-charge influences on peptide interaction (Vinson et al. 1993,
O’Shea et al. 1993, and O’Shea et al. 1992). But the effects of these contributions could be
modulated by competing intra-helical interactions with residues at b or c positions. Without
extensive mutational studies, or modeling that is capable of accurately assessing the
conformations of solvent exposed residues (Yadav et al. 2006), it will be difficult to firmly
establish which mechanisms are most important for changing the specificity of bZIP-binding
peptides or potentially even their binding geometry in complex with a given bZIP. In this work,
the oligomerization states of many of the best design-target complexes were determined, and
these were shown to be dimers (Table 2.3). The structures of unanticipated off-target complexes
are not known. Our results suggest that although it is beneficial to increase the predicted helicity
of designed peptides, this is best done using sequences that do not introduce large perturbations
to the charge patterns. We hope that better predictive theories may make it possible to design
surface charges to reinforce target binding and disfavor interactions with competitors.
We showed that peptides designed to bind human protein bZIP domains interacted tightly
and quite specifically with their targets in solution. OPTanti-FOS was also demonstrated to
inhibit FOS binding to DNA as a heterodimer with JUN. Reagents capable of selectively
inhibiting bZIP function may find utility in unraveling the roles of distinct proteins in gene
regulation. Transfection studies with rationally designed A-ZIPs or with dominant-negative
mutants lacking the transactivation domain are precedents for this type of application (Ham et al.
1995 and Olive et al. 1997). Previously used inhibitors relied on a native zipper domain to
provide binding specificity. However, published data describing the interactions each bZIP can
make in vitro reveal complex interaction profiles and indicate that relying on a native zipper
78
domain for inhibition is not ideal (Newman and Keating 2003 and Reinke et al. 2013). For
example, according to data published by Reinke et al., the affinity of the JUN-FOS heterodimer
is similar to the affinity of the JUN-BATF and JUN-BATF3 complexes. Therefore, fusing an
acidic extension to JUN to specifically target FOS could lead to off-target effects if FOS, BATF,
and BATF3 are expressed in the same tissue (Ravisi et al. 2010 and Reinke et al. 2013).
Targeting CREBZF using a native bZIP domain confronts similar issues. Not a lot is known
about the roles of genes regulated by CREBZF, and a reagent capable of specifically targeting
CREBZF would thus be useful. However, all of the in vitro native bZIP binding partners of
CREBZF, including XBP1, ATF6B, NFE2L1, and ATF4, either form stronger homodimers than
the heterodimeric interaction with CREBZF or bind more tightly to another bZIP, making them
non-ideal as the basis for selective inhibitor design (Reinke et al. 2013). Our results indicate that
peptides with novel and very specific interaction patterns can be designed and further optimized
to provide customized reagents. This should be possible not only for bZIP transcription factors,
but also for other coiled-coil targets in humans, pathogens, and model organisms (Barth et al.
2008).
79
Additional tables
Table 2.5
Table 2.5 Measured affinities for designed peptides at 37 °C. Kd listed is in nanomolar. A “*” denotes a fitted Kd
with R2 value < 0.8.
Table 2.6
Table 2.6 Measured affinities for designed peptides at 23 °C. Kd listed is in nanomolar. A “*” denotes a fitted Kd
with R2 value < 0.8.
80
Table 2.7
Table 2.7 Measured affinities for designed peptides at 4 ° C. Kd listed is in nanomolar. A “*” denotes a fitted Kd
with R2 value < 0.8.
Table 2.8
Table 2.8 Calculated affinities for OPTanti-XBP1_A at 37, 23, and 4 °C in 400 mM KCl. Kd is in nanomolar.
81
Table 2.9
Table 2.9 Calculated affinities for OPTanti-XBP1_B at 37, 23, and 4 °C in 400 mM KCl. Kd is in nanomolar.
Table 2.10
Table 2.10 Calculated affinities for OPTanti-XBP1_B-GLN at 37, 23, and 4 °C. Kd is in nanomolar.
Methods
Surface Redesign
Surface residues at positions b, c, and f were chosen in a manner similar to that described
in (Grigoryan et al. 2009). Briefly, with the core a, d, e, and g residues fixed, surface residues
were originally chosen to complement the core residues by optimizing an approximation of the
conditional probability P(si|c1, c2, …cn) for each surface position si given the core residues
c1…cn, based on residue frequencies in a dataset of 432 bZIP sequences. Constraints were placed
on total helix propensity (HP, negative values reflecting more favorable free energy in the helical
state) as given by (O’Neil and DeGrado 1990), on total number of charged residues (NQ), and on
82
total net charge (netQ). The values allowed for these properties were the range [µ−σ, µ +σ],
where µ was the average value of the property calculated from the bZIP sequence dataset and σ
was the standard deviation (24). For the surface redesign, the same bZIP sequence dataset was
used to calculate residue-pair frequencies and average peptide charges and helix propensities,
and the same conditional probability expression was maximized. Constraints were still placed on
netQ, NQ, and HP. Two additional constraints were placed on electrostatic energies Ei,i+3 and
Ei,i+4, between residues in positions i, i+3 and i, i+4, based on energy values from Table 2 from
(Munoz and Serrano 1994). To favor helix formation, the original constraint ranges were relaxed
to allow incorporation of more residues with greater helical propensity and more charged
residues that could form potential salt bridges. The design process was run iteratively.
Constraints on the five quantities described above had allowed ranges [µ-δσ, µ] for HP Ei+3, and
Ei+4; [µ, µ+δσ] for NQ; and [µ-δσ, µ+δσ] for netQ. Delta (δ) was set to 0.5 and was increased
after each design iteration by 0.5. All sequences were saved and analyzed.
Cloning and Purification
Genes synthesized using E. coli optimized codons were designed with DNAworks
(Hoover and LubKowski 2002). Primers were ordered from Integrated DNA Technology. Genes
were constructed and digested with BamHI and XhoI (New England Biolabs), and cloned into a
modified pDest vector with an N-terminal 6-histidine tag followed by the linker
GESKEYKKGSGS to help with peptide solubility (Reinke et al. 2010). An additional GC was
added to the C-terminus for labeling. All clones were sequence verified before expression.
Designs were expressed in E. coli RP3098 cells by growing in 1 L of LB at 37 °C to an OD at
600 nm of 0.4 - 0.6, and then induced with 1 mM IPTG. Cells were grown for an additional 4
hours after induction and then pelleted. Proteins were purified on a Ni-NTA column under
denaturing conditions followed by reverse-phase HPLC using a linear acetonitrile gradient, after
which they were lyophilized and sent for MALDI mass spectrometry analysis.
83
Circular Dichroism (CD) Spectroscopy
CD measurements were taken on an AVIV Model 420 Spectrometer. Proteins were
diluted to 10 µM in 1x PBS pH 7.4 (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM
KH2PO4) + 1 mM DTT and incubated at room temperature for at least two hours before being
measured. Wavelength scans were taken at 25 or 37 °C and performed in a 1 mm cuvette. Signal
was monitored from 200 nm to 260 nm in 1 nm steps with an averaging time of 5 seconds at each
wavelength. Three scans were taken for each sample and the final signal used in the analysis was
the average of the three scans corrected for the buffer signal.
Analytical Ultracentrifugation
Equilibrium AUC was performed using a Beckman XL-I centrifuge with interference
optics. The designed peptides and coiled-coil target sequences used are listed in Table I. Cloning
and purification of the constructs using a modified pDest vector are described above. Individual
proteins were dialyzed three times against a reference buffer (PBS + 1 mM TCEP-HCl)
including at least once overnight. After dialysis, concentrations were determined, and equalmolar mixtures of the unlabeled design with the coiled-coil target were mixed to give total
peptide concentrations ranging from 40 µM to 200 µM. Rotor speeds of 28,000, 35,000 and
45,000 rpm were used at 20 °C. Each spin was at least 20 hours, and equilibrium was ensured
before measurements were taken by checking that there was little difference in signal change
between sequential scans. Parameters for protein partial specific volume, buffer viscosity, and
buffer density were calculated using the Sednterp web server (Biomolecular Interaction
Technologies Center). Data were analyzed using Sedfit (Schuck et al. 2002). Each concentration
was fit individually using data from all three spin speeds, and the best-fit molecular weight was
calculated for a single species. The fitted molecular weights reported are for 40 µM total peptide
concentration.
Labeling of Designs and Targets
84
Designs were labeled with either fluorescein-5-maleimide or Rhodamine Red-C2maleimide (Invitrogen Life Technologies) for the FRET assay. For labeling, the protein was
reduced in 1 mM TCEP-HCl (Pierce Technology), buffer-exchanged into degassed PBS, and
incubated overnight at room temperature with 10-fold excess fluorophore (Thompson et al.
2012). Free dye was removed using an Ni-NTA column, and protein eluted with 0.1%
trifluoroacetic acid was lyophilized, resuspended, desalted using a spin-column (Bio-Rad), and
stored in 10 mM potassium phosphate buffer, pH 4.5, at -80 °C. Peptide concentrations were
measured in 6 M guanidine-HCl/100 mM sodium phosphate pH 7.4 using the absorbance of the
dye with an extinction coefficient of 68,000 M-1 cm-1 at 499 nm for fluorescein and 119,000 M-1
cm-1 at 560 nm for rhodamine (Reinke et al. 2013 and Thompson et al. 2012). These
concentrations were in good agreement (within 2-fold) of protein concentrations determined at
280 nm using the extinction coefficient calculated for the peptide and taking into account
contributions from the dye. Fluorescein- and TAMRA-labeled target bZIP proteins were
prepared as previously described (Reinke et al. 2013).
Direct FRET assay and Competition FRET assay
Both the direct and competition assays were performed in 384-well plates and were
similar to the manual assays described in (Reinke et al. 2013). Wells were filled with 20 μL of 1
mM TCEP-HCl. A 4 μM stock of rhodamine-labeled protein (FRET acceptor) was made fresh in
1 mM TCEP-HCl and was added to provide 2-fold dilutions in a constant volume of 20 μL
across 12 wells. Twenty microliters of a 40 nM stock of donor-labeled protein was then added to
the wells, and then 40 μL of 2x binding buffer was added to the wells and mixed, for final
concentrations of 10 nM donor-labeled protein and acceptor-labeled protein ranging from 0 to 1
μM in a total volume of 80 μL in 150 mM KCl, 50 mM potassium phosphate pH 7.4, 0.1 %
85
Tween-20, 0.1 % BSA. For the high-salt experiment in Figure 3C, the final KCl concentration
was 400 mM. Plates were incubated for one hour each at 4, 23, and 37 °C, after which the plates
were read at an excitation wavelength of 480 nm monitoring emission at 525 nm. Equilibration
was assessed by measuring donor fluorescence over an 8-hour time course, during which time
the signal did not change.
A subset of interactions for each design tested was repeated to ensure reproducibility of
the assay. All repeated experiments gave Kd values within two-fold.
In the competition form of the assay, a mixture of 40 nM of the donor, 40 nM of the
acceptor, and 160 nM of DNA was prepared in 1 mM TCEP-HCl in water. Unlabeled design was
titrated in 2-fold dilutions over the wells, and then 20 µL of the donor-acceptor-DNA mixture
was added to the wells. The 2x form of the buffer described above was then added for final
concentrations of 0-500 nM unlabeled peptide, 10 nM donor peptide, 10 nM acceptor peptide,
and 40 nM DNA. The consensus AP1 DNA site used for the experiment was 5’CGCTTGATGACTCAGCCGGAA-3’. Mixtures were incubated for at least one hour before
measurement. Equilibration was assessed by measuring the donor fluorescence four hours later,
over which time the signal did not change. The competition experiments were done in triplicate.
Data analysis
The heterodimer equilibrium dissociation constants were calculated as described in
(Ashenberg et al. 2011 and Reinke et al. 2013). The design peptide homodimer dissociation
constant was experimentally determined and used with previously reported human bZIP
homodimer dissociation constants measured for these same protein constructs (Reinke et al.
2013) to calculate the heterodimer dissociation constants. All heterodimer dissociation constants
were calculated using homodimer dissociation constants measured under the same conditions,
except the NFE2L3 homodimer under 400 mM KCl. In this case, the homodimer of NFE2L3
determined at 150 mM KCl was used to fit the NFE2L3 heterodimer Kd values at 400 mM KCl.
86
Fitting was done by simulating the experiment using a system of ordinary differential equations
that considered whether the donor, acceptor, or both, formed homodimers. The program used
was written in Matlab and is described in (Ashenberg et al. 2011). Given homodimer Kd values,
a trial Kd value, and the concentration of the fluorescence donor, the program iterated over each
acceptor concentration to determine the expected concentration of each potential homodimer and
heterodimer. The Kd for the heterodimer interaction was assigned as the value for which the
simulation best matched the experimental data. The lowest Kd value considered in data analysis
was 1 nM, and based on manual inspection of the FRET curves, all interactions with a best-fit Kd
of 1.0 nM were classified as < 1 nM. For experiments done at 37 °C, the largest Kd value
simulated was 5000 nM. For experiments done at 4 and 23 °C, the largest value was 1000 nM.
When the best-fitting Kd value was the maximum value (1000 or 5000 nM), then if the R2 value
for the agreement of experiment with data was > 0.8 that value was reported. If the fit was less
good, with R2 < 0.8, the Kd was reported as a bound on the upper limit, based on manual
inspection of curves, e.g. >1000 nM or > 5000 nM. Eighty percent of the interactions tested that
were assigned a Kd value less than the maximum of 1000 or 5000 nM had an R2 value > 0.9.
For the competition experiment, IC50 values were determined by fitting the binding data
to a logarithmic inhibition equation: signal = a + (b-a)/[(1+x/c)]^H, where a and b are the lower
and upper baselines, c is the IC50 value and x is the inhibitor concentration. The Hill coefficient
H was set to 1.
Comparisons to the Array
Interactions were compared in a binary manner. In the array study, interactions with Sarray
> 2.5 were classified as interactions, and values below this cutoff were assigned as noninteractions (Grigoryan et al. 2009). In the FRET assay, bZIP pairs were assigned as noninteracting if the dissociation constant was > 1000 nM. For agreement between the assays, we
87
considered (1) FRET interactions with Kd < 1000 and Sarray > 2.5 or (2) FRET interactions with
Kd > 1000 and Sarray < 2.5. Interactions detected only on the array had Sarray > 2.5 and Kd > 1000.
Interactions detected only by FRET had Kd < 1000 and Sarray < 2.5.
88
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Chapter 3
Data-driven prediction and design of bZIP coiled-coil
interactions
Portions of this chapter are currently being combined with other work by Dr.
Vladimir Potapov into a manuscript to be submitted later.
Collaborator notes:
Vladimir Potapov initiated the project, trained and benchmarked the model,
developed the design protocol, and contributed a significant portion to all sections
of this chapter.
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Introduction
In the previous chapter, a two-step design strategy for coiled coils was used. The first
design step optimized the core a, d, e, and g positions for interaction specificity and affinity
(Grigoryan et al. 2009). The second step incorporated helix-promoting residues into the surface
b, c, and f positions to increase interaction affinity. This strategy was guided by the coiled-coil
structure. Core residues point inward towards the interface and are primarily involved in
specificity and affinity of interactions (O’Shea et al. 1992 and O’Shea et al. 1993), and surface
residues point outward into the solvent (see Figure 2.1). However, another way to view a coiledcoil dimer is as an alignment of helices. Each heptad in one coiled-coil chain is optimized for
binding the opposite heptad in the second coiled-coil chain (Figure 3.1). As a design strategy,
this would involve (1) breaking down the design-target interaction into pairs of heptad
interactions and (2) using a scoring function to calculate residue interactions between all heptad
pairs. In this chapter, I experimentally validated a new machine-learning scoring function for
coiled-coil prediction that Dr. Vladimir Potapov developed and benchmarked. This function was
implemented into a protocol that generated designed peptides by concatenating native bZIP
heptads predicted to interact strongly with the heptads in the target bZIP protein.
Figure 3.1
Figure 3.1 Cartoon of the model bZIP GCN4 as interacting heptads. Alignment of the heptads of GCN4 with
some interactions between heptads indicated by arrows (PDB: 2ZTA from O’Shea et al. 1991).
94
Different scoring functions have been used to predict whether coiled coils interact given
the sequences as input. In the previous chapter, the HP/S/C function used structure-based
modeling of pair-wise residue interactions supplemented with experimental data of known
residue-residue interactions (Grigoryan et al. 2009). Other scoring functions include (1) the
summation of Vinson coupling energies discussed in the previous chapter for residue interactions
in the a-a’ and e-g’ positions (Krylov et al. 1994, Acharya et al. 2002, Acharya et al. 2006, and
Grigoryan and Keating 2006). (2) A support vector machine-learning method that used known
coiled-coil sequences and experimental data to generate additive weights for the interacting
residue pairs at the aia’i, did’i, aid’i, dia’i+1, die’i, gia’i+1, and gie’i+1 positions (Fong et al. 2004).
(3) bCIPA, an additive model that incorporated the Vinson coupling energies, simple
electrostatic interactions in the e-g’ positions, and a helix propensity scale to predict thermal
stability of coiled coils (Hagemann et al. 2006). All of these scoring functions incorporate
experimental information and were tested on experimental datasets, including predicting the
stabilizing or destabilizing effects of mutations in model systems (Hagemann et al. 2006) and
ranking the stabilities of bZIP dimers (Fong et al. 2004, Grigoryan and Keating 2006). Even the
simple summation of the Vinson coupling energies, which only considers 61 pairs of amino acids
at positions a-a’ and g-e’ (Krylov et al. 1994, Acharya et al. 2006), was ~70% successful in
ranking bZIP dimer stabilities (Grigoryan and Keating 2006), showing the importance of
experimental information for generating good predictive models.
Recently, a large-scale study determined solution affinities for the pair-wise interactions
of over 200 bZIP proteins (Reinke et al. 2013). bZIPs were identified in seven species, and pairwise measurements were determined within species and between species. Given that the limited
experimental data by Acharya et al. and Krylov et al. led to a model that could discriminate
95
between high-affinity coiled coils and non-interacting pairs, Dr. Vladimir Potapov developed a
new scoring function using the experimental data from the Reinke et al. study. This model was
compared to previous models and then included in a design protocol to generate new peptides
predicted to be specific for a given bZIP target.
Results
Model benchmarking
The dataset included over 5,000 bZIP dimers with an associated equilibrium dissociation
constant (Reinke et al. 2013). The data used to train the model was split into “binders” (Kd <
5000 nM) and “non-binders” (Kd > 5000 nM), which compromised 20 and 80%, respectively, of
the experimental dataset. A machine-learning approach was used to generate weights for residue
pairs and residue triplets. The value of a weight represented the contribution of a residue pair or
triplet to coiled-coil stability, and the final predicted interaction score for two input sequences
was the sum of the calculated weights.
Two prediction tests were used to benchmark the new model, referred to as SVR, and this
model was compared to the previously published HP/S/C, Fong, and bCIPA models, and to the
Vinson coupling energies. In the first test, experimentally determined affinities (1 nM <Kd <5000
nM) were compared to prediction scores. A Pearson correlation coefficient (R) was reported for
this test to provide a measure of the linear correlation between the predicted scores and the
experimental affinities. In the second test, the data were divided into strong interactions (Kd <
250 nM) and weak/non interactions (Kd > 5000 nM) to test whether the model classified bZIP
dimers into the correct category. The area under the curve for a receiver-operating characteristic
(ROC) curve was reported as a measure of the model’s performance as a binary classifier (Table
96
3.1). The new SVR model was the most successful predictor both in terms of the linear
correlation (R) and the area under the curve (AUC) for binary classification of tight binding/nonbinding. The model also showed significant improvement over the next best predictor, the Fong
machine-learning model from Fong et al.
Table 3.1
Table 3.1 Comparison of previously published predictive models to the new machine-learning model. The data
from the pair-wise interactions of bZIPs in 7 species were used in the tests. Correlation was measured between 573
Kd’s and predicted scores (R value). The binary classification considered 3,065 total binders and non-binders and the
area under the curve (AUC) is reported for a ROC curve. The Fong model is from Fong et al. 2004. bCIPA is from
Hagemann et al. 2006. The Vinson model adds the coupling energies for residues at a-a’ and g-e’ generated in
Krylov et al. 1994 and Acharya et al. 2006 and used in Grigoryan and Keating 2006. HP/S/C is a structure-based
scoring function from Grigoryan and Keating 2006.
Designing specific binders
Attractive features of the SVR model prompted us to test it in computational protein
design applications. Our model does not require explicit modeling of protein complexes but
instead takes as input the sequences of two bZIP proteins. The simple procedure of summing
contributions from pairs and triplets of residues makes scoring extremely fast, which is a benefit
when exploring large sequence spaces. Despite the computational efficiency of this approach, it
is highly accurate, as discussed above.
In our design work, the goal was to engineer coiled-coil peptides that could bind
selectively to particular bZIP proteins. This type of specificity design has been attempted
previously using the HP/S/C scoring function, with good success for a range of bZIP targets
(Grigoryan et al. 2009). In our procedure, sequences of designed proteins were assembled from
97
short sequence fragments derived from natural bZIP proteins. We reasoned that constraining our
designs to resemble the native proteins on which our prediction algorithm was trained, and on
which it showed good performance, would improve our chances of success. Nevertheless, the
sequence space that we chose for design was very large. Our library of 7-residue fragments
included 1,303 heptads from 440 proteins. Assuming that the average length of the coiled-coil
motif is 42 residues (6 heptads), the total search space was ~5x1018 possible sequences. The
design procedure consisted of using integer linear programming (ILP) to find the sequence with
the lowest (most favorable) predicted interaction score with the intended target, under the
constraint that all scores for off-target bZIPs be above a predefined cutoff (Figure 3.2, see
Methods for details).
Figure 3.2
Figure 3.2 Highlights of the scoring method used in the design process. Heptads within the library were scored
against each heptad in the target bZIP and all specified off-target bZIPs. Integer linear programming was used to
minimize the predicted score of the assembled sequence for the target under a constraint to keep off-target scores
above a specified cutoff. Complete details of the design process including constraints are in the Methods.
We chose five bZIP proteins as targets: X-box binding protein 1 (XBP1), JUN,
Activating Transcription Factor (ATF) 3, ATF4, and ATF5 (Table 3.2). These are proteins for
98
which an interaction partner was successfully designed in prior work, but either the affinity or
the specificity of the design left room for improvement (Grigoryan et al. 2009). Additionally,
these proteins play important roles in stress responses, tissue differentiation, and the unfolded
protein response (Hai and Hartman 2001, Shaulian & Karin 2002, Angelastro et al. 2003, and
Calfom et al. 2002), and having reagents to study selective inhibition of these bZIP proteins may
be therapeutically useful. In particular, prior work targeting XBP1 led to designed coiled coils
with very low affinity for XBP1 (Grigoryan et al. 2009). We designed nine sequences targeting
these proteins, using the protocol described in the Methods. The designed sequences were
cloned, purified, and labeled with a fluorophore at either the N- or C-terminus to test for binding
to a panel of 32 representative bZIP proteins (see Methods).
Table 3.2
Table 3.2 Sequences of bZIP targets and designed peptides used in this study. bZIP targets were XBP1, JUN,
ATF3, ATF4, and ATF5.
To determine whether the designed peptides could bind to their intended partners, designs
were labeled with rhodamine at the C-terminus (see Methods) and tested in a direct-binding
FRET assay against a C-terminally fluoresceinated bZIP construct (Reinke et al. 2013). Eight of
99
nine designed peptides bound to their target at one or more of the temperatures tested (4, 23, or
37 °C), and four peptides were chosen for further characterization (Table 3.3). Anti-XBP1, antiJUN, anti-ATF4, and anti-ATF5 bound their targets with affinities of 170 nM, 5 nM, 10 nM, and
730 nM, respectively. During the design process, interaction with closely related family
members was not penalized, and we observed that anti-ATF5, which was designed to bind to
ATF5, bound more tightly to related family member ATF4 (Kd = 6 nM). This is consistent with
the predicted computational score of the design interacting with ATF5 and ATF4 (0.75 vs. 0.40,
respectively).
Table 3.3
Table 3.3 Tested anti-bZIP designed peptides and calculated affinities. 1 Four designs were chosen for further
characterization. The “a” is dropped from anti-XBP1a and anti-JUNa for simplicity in future references. 2 Although
ATF5 was used in the computational design, binding was tighter to ATF4, which shares 69.2 % sequence identity
with ATF5 in a, d, e and g positions.
To confirm the oligomerization of the design-target complexes, equal molar mixtures of
the design and target were analyzed by equilibrium analytical ultracentrifugation (see Methods).
The data were collected at multiple concentrations above the experimentally determined Kd value
and at several rotor speeds for each mixture. In each case, the best-fit molecular weight
corresponded to that expected for a dimeric species (Table 3.4). These data, in combination with
the FRET analysis, indicated that the designs bound their targets to form heterodimers, as
intended.
100
Table 3.4
Table 3.4 Molecular weights of the design-bZIP complexes determined by analytical ultracentrifugation. The
complexes were in a 1:1 ratio of design:target. See Methods for details.
To determine the helix orientation of the design-target complexes, an N-terminally
labeled rhodamine construct was made. Both N- and C-terminally labeled designs were tested for
binding to the C-terminally fluoresceinated target, and the FRET efficiencies of the different
complexes were compared. In all four cases, the FRET efficiency was higher when the
fluorophores were located at the same terminus, supporting a parallel alignment of helices. We
also observed that the affinities of N-terminally labeled designs for C-terminally labeled targets
were weaker (Table 3.5). Enhanced affinities attributable to dye interactions have been reported
previously (Reinke et al. 2013). To assess whether the designed peptides could bind tightly to
their intended targets without any contribution from the dye, a competition assay was performed
using unlabeled designed peptides to inhibit a FRET complex between the design and target (see
Methods). KI values were calculated in the cases where neither the designed peptide nor target
formed strong homodimers, and in one case the KI value was seven-fold higher than the Kd
calculated by direct binding, consistent with a previous report (Reinke et al. 2013) (Table 3.6).
Table 3.5
Table 3.5 Calculated FRET efficiencies and affinities of design-target interactions. 1 FRET efficiency. Kd’s
determined at 37 °C with the acceptor label at the N- or C-terminus of the designed peptide. All bZIP constructs
used had the dye at the C-terminus.
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Table 3.6
Table 3.6 Calculated inhibition constants (KI) for the designed peptide inhibiting a FRET complex of the
design-target interaction.
To determine the interaction specificity of the bZIP-targeting designs, both N- and Cterminally labeled peptides were tested for binding to 32 different C-terminally fluorescinated
bZIPs at three different temperatures (Figure 3.3 and Tables 3.8-3.10 after the Discussion). This
combination of tests potentially allows detection of either parallel or anti-parallel coiled-coil
interactions between the design and off-target bZIPs, even if FRET is inefficient between dyes
located at opposite ends of a coiled coil. Only parallel interactions were considered in the
computational design protocol, but any off-target binding would be undesirable. Figure 3.3
reports the tightest interaction observed between each off-target bZIP with either an N- or Cterminally labeled design at 37 °C. All designs bound to the intended bZIP family most tightly,
with anti-JUN showing 100-fold specificity for JUN, anti-ATF4 and anti-XBP1 showing 30-fold
specificity for ATF4 and XBP1, and anti-ATF5 about 20-fold specific for ATF4 at 37 °C. In all
four cases, greater than 80% of off-target interactions were avoided at 37 ºC. anti-JUN avoided
30/31 off-targets, anti-ATF4 avoided 27/31, anti-XBP1 avoided 32/32, and anti-ATF5 avoided
25/31. Interestingly, a few interactions were detected more tightly with the N-terminally labeled
design than the C-terminally labeled design, suggesting a possible anti-parallel interaction.
Further analyses of these interactions with a helical-wheel diagram between anti-ATF5 and
XBP1, for example, in the parallel state indicated two net potentially repulsive salt bridges
forming between the core e and g residues, while in the anti-parallel state there was a net of two
potentially favorable salt bridges forming. Additionally, the FRET efficiency of the anti-ATF5
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and XBP1 interaction when the designed peptide was labeled at the N-terminus was larger than
when the designed peptide was labeled at the C-terminus (0.19 vs 0.05, respectively), also
suggestive of an anti-parallel interaction.
Figure 3.3
Figure 3.3 Specificity profiles at 37 °C of the designs anti-XBP1, anti-JUN, anti-ATF4, and anti-ATF5.
Sequence similarities calculated as in (Pearson et al. 1997) between the target and other bZIPs are indicated in
greyscale and were calculated using only core residues. “**” indicates the interaction was detected with the acceptor
fluorophore at the N-terminus of the designed peptide. Names of all proteins and affinities are listed in Table 3.8
after the Discussion section.
For anti-ATF4, a homodimer with a Kd of 15.5 nM and 1 nM at 23 and 4 ºC, respectively,
we repeated some of the binding experiments by titrating the native bZIPs into the design, rather
than the opposite. This allowed us to keep the design fixed at a low concentration, potentially
making it easier to detect interactions with a labeled bZIP. In this test, no new quantifiable
interactions were detected. In four cases, a complex signal was observed at 4 °C that could not be
fit with the given model and suggested a different orientation or oligomerization was possibly
forming (Table 3.7). In these cases, the signal showed an increase in donor fluorescence as the
acceptor-labeled human bZIP was titrated in. One potential cause of this signal is an anti-parallel
interaction occurring with the tightly homodimerizing donor-labeled peptide. In the absence of
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an acceptor-labeled bZIP protein, the designed donor-labeled peptide homodimerized and
quenched the fluorescence. This quenching was then alleviated as the acceptor-labeled bZIP was
titrated in and competed with the homodimer, but FRET did not occur because the fluorophores
were at opposite termini. For these bZIP proteins CREBZF, ATF3, BATF, and DBP, anti-ATF4
was tested in a competition experiment to determine if the unlabeled designed peptide could
inhibit a FRET complex containing the candidate binding partner at the physiologically relevant
temperature of 37 °C, but no inhibition was detected (Figure 3.4).
Table 3.7
Table 3.7 Affinities determined with anti-ATF4 as the FRET donor. The anti-ATF4 peptide was labeled at the
C-terminus with a donor fluorophore and the bZIPs were labeled at the C-terminus with an acceptor fluorophore. Kd
listed in nanomolar. "NI" indicates no interaction was detected up to 1 µM of acceptor protein. "ND1" indicates a
change in donor fluorescence was detected but too weak to quantify a Kd. "ND2" indicates the model used for fitting
could not be applied. See Methods for how the fitting was performed.
One application of designed anti-bZIPs is to inhibit bZIP dimerization. To determine
whether the designed peptides could function as inhibitors, unlabeled design was mixed with the
components of a FRET complex containing the intended target (see Methods). We tested antiJUN for inhibition of JUN with FOS, anti-XBP1 for inhibition of XBP1 with CREBZF, and anti-
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ATF4 for inhibition of ATF4 with FOS. In all cases, the designs decreased FRET signal from the
target complex (Figure 3.5). Of note, the anti-JUN design was tested to see if it could compete
with the FOS-JUN heterodimer, a functionally relevant complex important in regulating cell
proliferation and when mis-regulated leads to oncogenic transformation (Shaulian & Karin
2002). The dissociation constant of the FOS-JUN heterodimer is less than 1 nM (Reinke et al.
2013), and the design inhibits this interaction with an IC50 of about 250 nM.
Figure 3.4
(a)
(b)
(c)
(d)
Figure 3.4 Lack of inhibition of off-target bZIP complexes by unlabeled anti-ATF4 at 37 °C. (a) The FRET
complex is 10 nM DBP-FITC with 30 nM DBP-TAMRA. (b) The FRET complex is 10 nM CREBZF-FITC with 20
nM CREBZF-TAMRA. (c) The FRET complex is 10 nM ATF3-FITC with 40 nM ATF2-TAMRA. (d) The FRET
complex is 10 nM BATF-FITC with 40 nM JUNB-TAMRA.
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Figure 3.5
(a)
(b)
(c)
Figure 3.5 Designed peptides acting as inhibitors of target bZIP complexes. (a) anti-JUN inhibiting 10 nM JUN
with 50 nM FOS with an IC50 of 251 nM at 37 °C. (b) anti-XBP1 inhibiting 10 nM XBP1 with 50 nM CREBZF with
an IC50 of 170 nM at room temperature. (c) anti-ATF4 inhibiting 10 nM ATF4 with 200 nM FOS with an IC50 of
347 nM at 37 °C. See Methods for assay details.
Discussion
The experimental bZIP interaction data from Reinke et al. and domain knowledge of the
coiled-coil motif were key components that allowed building an accurate prediction model.
Although this model is system-specific and cannot be applied to other coiled-coil systems, it
contributes to our understanding of different mechanisms of bZIP specificity and might help to
devise more general models in the future.
In the model, we accounted for both pair-wise and three-residue interactions to account
for cooperative interactions in the coiled-coil motif. Cooperativity is known to play an important
role in biomolecular recognition. However, it is complex to model and often neglected. We tried
106
to address cooperativity by considering simultaneously three spatially adjacent residues in the
coiled-coil interface. The features and weights in the model can be interpreted as the contribution
of a particular residue pair or triplet to the stability of the coiled-coil interaction. Analysis of the
magnitudes of the triplet weights indicated that significant weights correspond to the
circumstances where a hydrophobic residue at an a position is buried by the salt bridge formed
between residues at e-g’ positions, which is a stabilizing interaction. Conversely, if two similarly
charged residues are at the e-g’ positions, the hydrophobic residue at a is exposed and
destabilizes the interaction.
Weights were only obtained for the residue features that were present in the training
dataset. If a particular residue pair or triplet was not represented or was represented
insufficiently, then that feature would be given a weight cloze to zero. On the other hand, a
particular feature might also be given a negligible weight if it genuinely has a small effect on
coiled-coil stability. Thus, the model relies on a sparse set of weights derived from the training
set. Therefore when applying the model to designing new interactions, it is desirable to enforce a
strategy in which the sequence space resembles the one used to derive the model, so as to
produce reliable predictions.
In our design procedure, we applied a modular approach that combined native heptads
into a novel full-length sequence. Because the heptads come from many different sequences and
may be mutually incompatible, we checked that the residues at the heptad junctions appeared in
native sequences (see Methods for details). Using naturally occurring heptads allowed us to
control the sequence space while keeping it vast. It had an additional benefit of simultaneously
taking care of the choice for residues at the surface b, c, and f positions. The derived model
scores residues at the core a, d, e, and g positions of the coiled-coil motif, and if heptads were
107
not used it would have been necessary to devise an algorithm to pick residues for the surface
positions.
We used integer linear programming (ILP) to obtain a final peptide sequence predicted to
bind a specified target and not interact with off-targets. ILP is an optimization method that
allows for minimizing a function given a set of constraints. This method is optimally suited for
designing specific interactions because we are minimizing the interaction score with the target
while keeping scores for all off-target interactions above a predefined cutoff. With a finite heptad
library, it is possible to pre-compute all elementary interactions and use the computed values to
define the optimization problem (see Methods for details). An important advantage of ILP is that
it finds a global minimum, thus giving the best possible binder using the scoring function and
constraints.
Our design strategy was very successful. Eight of nine (89%) designs bound their
intended targets, and four (44%) of the most promising designs taken for further characterization
formed tight and specific interactions. A close examination of the anti-XBP1b design, which did
not bind detectably to XBP1, revealed two positions that could be mutated to potentially form a
more stable interaction with XBP1. A tyrosine at an a position in anti-XBP1b could be
destabilizing, but because it is relatively rare we cannot derive a reliable weight for interactions
involving tyrosine at a positions. Mutating this tyrosine to leucine and an adjacent serine at an e
position to lysine to form a favorable e-g’ interaction with XBP1 recovered some of the binding
affinity for XBP1 (Kd of 1545 nM at 37 °C and 125 nM at 23 °C). Therefore, although the
strategy used was remarkably successful for scoring and designing specific bZIP coiled-coil
interactions, there is evidence of its limitations due to the sparseness of weights. Analysis of
residue pairs and triplets given low scores or scores of zero can be analyzed for frequency within
108
the dataset to determine if the score comes from lack of experimental data or if the feature
simply does not contribute to stability. With this analysis, the computational method can guide
the experiments, eventually leading to an even larger dataset and better predictions.
Additional tables
Table 3.8
Table 3.8 Calculated affinities at 37 °C. Interactions were detected between both an N- and C-terminal acceptorlabeled designed peptide and a C-terminal donor-labeled bZIP protein. The Kd (in nM) listed was the strongest
interaction detected. "**" indicates the Kd listed was detected with the N-terminally labeled design. "NI" indicates
no interaction was detected up to 1 µM of acceptor protein. "ND1" indicates a change in donor fluorescence was
detected but too weak to quantify a Kd. See Methods for how the fitting was performed.
109
Table 3.9
Table 3.9 Calculated affinities at 23 °C. Interactions were detected between both an N- and C-terminal acceptorlabeled designed peptide and a C-terminal donor-labeled bZIP protein. The Kd (in nM) listed was the strongest
interaction detected. "**" indicates the Kd listed was detected with the N-terminally labeled design. "NI" indicates
no interaction was detected up to 1 µM of acceptor protein. "ND1" indicates a change in donor fluorescence was
detected but too weak to quantify a Kd. See Methods for how the fitting was performed.
110
Table 3.10
Table 3.10 Calculated affinities at 4 °C. Interactions were detected between both an N- and C-terminal acceptorlabeled designed peptide and a C-terminal donor-labeled bZIP protein. The Kd (in nM) listed was the strongest
interaction detected. "**" indicates the Kd listed was detected with the N-terminally labeled design. "NI" indicates
no interaction was detected up to 1 µM of acceptor protein. "ND1" indicates a change in donor fluorescence was
detected but too weak to quantify a Kd. See Methods for how the fitting was performed.
111
Methods
Benchmarking
The models listed in Table 3.1 were used to calculate stability scores for the bZIP coiledcoil interactions in the dataset. The sequence-based methods rely on scoring interactions between
residues in different positions within the coiled-coil motif. The method of Fong et al. (referred to
as Fong in Table 3.1) was developed to distinguish between coiled-coil binders and non-binders
(Fong et al. 2004). A set of 1325 computationally optimized weights were derived accounting for
pairs of residues in aia´i, did´i, gie´i+1, aid´i, dia´i+1, die´i, gia´i+1 positions. We applied this set of
weights to scoring coiled-coil interactions during the benchmark testing. Similarly, we used a set
of 61 coupling energies determined experimentally for most commonly occurring residues at
aia´i and gie´i+1 positions by Vinson and colleagues (referred as Vinson in the Table 3.1) (Krylov
et al. 1994, Acharya et al. 2006, and Grigoryan and Keating 2006). We used a web-server to
compute scores using the bCIPA algorithm, which was developed to predict coiled-coil thermal
stabilities given the sequences as input (Hagemann et al. 2008).
For the structure-based HP/S/C scoring function, we first used the Crick coiled-coil
parameterization server to generate a backbone template based on the crystal structure of GCN4
(PDB ID: 2ZTA from O’Shea et al. 1991) as was done previously in Grigoryan and Keating
2006. We then used an in-house HP/S/C algorithm to generate structural models and calculate
coiled-coil stability scores.
Design procedure
The design procedure used seven-residue sequence fragments (heptads) to assemble the
designed sequences. 1,303 unique heptads were extracted from ~400 bZIP sequences to
112
compromise the design heptad library. All heptads started with a residue at position f and ended
at position e. To pre-compute the interactions between all heptads in the library and all bZIP
proteins, a bZIP leucine-zipper domain was split into seven-residue heptads starting at position f.
Interactions between all heptads in the library and all heptads within each bZIP sequence were
scored and recorded. Additionally, we scored interactions between all consecutive heptad pairs
within the library with two consecutive heptads in each bZIP to account for the triplets terms
used in the scoring function. One consequence of this was the score of two consecutive library
heptads with two consecutive heptads in the bZIP not being equal to the sum of the individual
heptad scores.
The final designed sequence was constructed from individual heptads that scored the
lowest (strong binding) for the intended target while scoring above a predefined cutoff for all
off-target proteins. Integer linear programming (ILP) was used to formulate and solve this
problem optimally. The overall score for any target protein can be expressed as follows:
M
N
M #1 N
N
j,k j,k
S = " " x ij sij + " " " zi,i+1
si,i+1 ,
i=1 j =1
i
j
(1)
k
where x ij is a binary decision variable corresponding to j-th library heptad in i-th position of the
!
j,k
target protein; sij is a score for j-th library heptad in i-th position; zi,i+1
is a binary decision
!
j,k
variable corresponding to j-th and k-th library heptads in i-th and (i+1)-th positions; si,i+1
is a
!
!
difference score for of j-th and k-th library heptads in i-th and (i+1)-th positions; M is a number
!
of positions in the target protein; and N is the number of heptads in the library.
A set of constraints is additionally imposed to ensure that only one heptad is chosen for
each position (Eq. 2) and to constrain the problem for faster convergence (Eq. 3).
N
"x
j
i
= 1, for all i,
(2)
j =1
113
!
N
N
"z
j
j,k
i,i+1
= xi ,
j
"z
j,k
i,i+1
= x ik ,
(3)
k
For a specified target, ILP finds the solution that minimizes Starget under the given
!
!
constraints
(Eqs. 2 and 3) such that any off-target interaction gets a score above a predefined
cutoff Soff-target > cutoff. This cutoff value can be increased or decreased to generate designs
predicted to be more specific/less stable or less specific/more stable, respectively.
A few filters were first applied to discard undesirable solutions and to improve ILP
convergence time. (1) Only heptads containing L, N, V, I, K, A, R, T in position a and L, H, V,
M, I in position d were considered. These residues are among the top 85% occurring residues at
a and d positions in native bZIP sequences. The final library contained 1,054 heptads after
discarding all heptads that did not meet these criteria. (2) N-terminal heptads (149 in total) could
only be used at the first heptad position in the designed sequences. (3) a-a’ and d-d’ interactions
between the designed sequence and the intended target had too occur more than once in strong
binders from the dataset (Kd < 250nM), and no strongly destabilizing pairs were allowed
(coupling energies > 1kcal/mol according to Acharya et al. (Acharya et al. 2006)). (4) No
asparagine residues were allowed at the a position of the first and last heptads of the designed
sequence. Asparagine at an a position is an important specificity element in bZIP sequences.
However, its destabilizing effect when paired (a-a’) with a hydrophobic residue can be tolerated
at sequence ends. These filters eliminated the majority of heptads that were unlikely to be part of
a sequence that would bind strongly to any desired target.
To avoid gross sequence biases in the designs, a number of constraints were imposed on
the overall composition of the designed sequences. These constraints were based on statistical
analysis of bZIP sequences: (1) histidine at the d position was allowed only in the C-terminal
heptad. (2) Only one methionine was allowed at the d positions. (3) Glutamate at the a position
114
was allowed only in the N-terminal heptad. (4) The total number of polar residues at a positions
within the sequence was limited to 4. (5) The upper limit on total individual residues at a
positions was set at two for asparagine, and at one for lysine, arginine, glutamate, threonine, and
alanine.
Cloning, purification, and labeling of designs and targets
Genes for the designs were constructed by gene synthesis using codon-optimized primers
generated by DNAworks (Hoover & Lubkowski 2002) and ordered from Integrated DNA
Technology. For cloning the leucine-zipper target constructs for the AUC analysis, genes were
subcloned from previously described constructs (Reinke et al. 2010). Genes were digested using
BamHI and XhoI (NEB) and ligated into a modified pDest vector containing an N-terminal 6histidine tag followed by the linker GESKEYKKGSGS to help with solubility (Reinke et al.
2010). An additional GC or CG was added to the C-terminus or N-terminus (between the
solubility linker and design sequence), respectively, for labeling. All clones were sequence
verified before expression. Designs were expressed in E. coli RP3098 cells by growing in 1 L of
LB at 37 °C to an OD at 600 nm between 0.4-0.6. At this point, protein expression was induced
by the addition of 1 mM IPTG and cells were grown for an additional 4 hours before being
pelleted. Cells were purified by Ni-NTA column under denaturing conditions followed by
reverse-phase HPLC using a linear acetonitrile gradient (Reinke et al. 2010) after which they
were lyophilized and sent for MALDI analysis.
Labeling with Fluorescein-5-maleimide® or Rhodamine RedTMC2-maleimide (Invitrogen
Life Technologies) was done as described (Thompson et al. 2012). Briefly, protein was reduced
in 1 mM TCEP-HCl (Pierce Technology), buffer-exchanged into degassed PBS pH 7.4 (137 mM
115
NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4), and incubated overnight at room
temperature with 10-fold excess fluorophore. After labeling, free dye was removed using an NiNTA column. Labeled proteins were then lyophilized, resuspended, desalted using a spin-column
(Bio-Rad), and stored in 10 mM potassium phosphate buffer, pH 4.5, at -80 °C.
Fluorescein- and TAMRA-labeled target bZIP proteins were generously donated by
Aaron Reinke and previously described (Reinke et al. 2013).
Direct FRET assay and competition FRET assay
The direct assay was performed manually in 384-well plates and is similar to a previously
described assay (Reinke et al 2013). Wells were filled with 20 µL of 1 mM TCEP-HCl. A 4 µM
stock of acceptor protein was made fresh in 1 mM TCEP-HCl, and 20 µL was titrated in 2-fold
dilutions over the wells. Twenty microliters of a 40 nM stock of donor-labeled protein was then
added to the wells, and then 40 µL of 2x binding buffer was added to the wells and mixed, for
final concentrations of 10 nM donor-labeled protein and acceptor-labeled protein ranging from 0
to 1 µM in a total volume of 80 µL.
In the competition form of the assay, a FRET complex consisting of 10 nM of the donor
and a specified concentration of the acceptor was mixed in 1 mM TCEP-HCl, and 20 µL of this
solution was added to the wells after unlabeled design was titrated in 2-fold dilutions over the
wells to a final concentration ranging from 0 to 2.5 µM. The amount of acceptor used varied for
each curve, taking into account the Kd of the FRET complex and formation of a good lower
baseline for fitting purposes. The experiments in Figure 3.5 were done in triplicate.
Data analysis
116
The direct-binding FRET data was fit as previously described (Reinke et al. 2013). The
Kd of the design homodimer was experimentally determined and used with previously
determined target homodimer Kd’s to calculate the heterodimer Kd. The Kd's used for the human
bZIPs were determined in Reinke et al. 2013. The fitting uses an ordinary differential equations
model that considers whether the donor (D), acceptor (A), or both, form homodimers. The
algorithm then iterates over each acceptor concentration to determine the concentrations of each
potential species A, D, A2, D2, and AD that describe the equilibrium of the given model. A
simulated data set is generated using the fluorescence values at each acceptor concentration, and
a Kd is calculated from this simulation. A Kd is assigned to an interaction when the percent
difference between the simulated Kd and true Kd is not more than five percent. The Kd, upper
baseline, and lower baseline were fit. A minimum of 15% change in the donor fluorescence was
required for a fit to be attempted. If this minimum was not met, the interaction was classified as
“NI” for no evidence of interaction detected up to 1000 nM of acceptor. All curves were
separately fit using an upper limit on the Kd of 5000 nM or 1000 nM and individually inspected
to ensure quality of fit and proper classification. For curves that passed the signal change cutoff
but were assigned the upper limit Kd, the interaction was classified as “ND1” if the R2 of the
curve was not greater than 0.8 to indicate that a change in signal was observed but the Kd was too
weak to quantify up to 1000 nM acceptor. If the R2 was greater than 0.8, the interaction was
assigned a Kd of >5000 if using an upper limit of 5000 gave a better R2 value than using an upper
limit of 1000. If using an upper limit of 1000 gave a better R2, the assigned Kd was 1000< Kd
<5000. For signal changes that did not appear to fit with the model of an acceptor being within
range to absorb the donor fluorescence, the interaction was classified as “ND2” to indicate an
interaction was likely occurring but could not be described with the given model. One example
117
of this is increasing concentrations of acceptor causing an increase in donor fluorescence. A
potential model to describe this interaction is the acceptor-labeled protein interacting in an antiparallel manner with a donor-labeled protein that forms a tight homodimer. At a fixed
concentration in the absence of acceptor, the homodimerizing donor self-quenches. As acceptorlabeled protein is titrated into the well, it binds to the donor, but due to the acceptor fluorophore
not being within range to absorb the fluorescence, donor RFUs increase.
For the competition experiments and calculation of the IC50’s, the data were plotted on a
semi-log plot and the derivative of the signal change was calculated with respect to
concentration. This first derivative was then fit to find the x value that gave the inflection point
using MATLAB. KI’s were calculated as in Fu et al. 2007.
Analytical ultracentrifugation
Equilibrium AUC was performed using a Beckman XL-I centrifuge with interference
optics. Individual proteins were dialyzed three times against a reference buffer (1xPBS pH 7.4 +
1 mM TCEP-HCl) including at least once overnight. Concentrations were taken after dialysis and
equal-molar mixtures of the unlabeled design with the leucine-zipper target were mixed in
concentrations ranging from 20 µM to 100 µM. Three different rotor speeds of 28,000 rpm;
35,000 rpm; and 45,000 rpm were run at 20 °C for at least 20 hours, and equilibrium was ensured
before measurements were taken by checking that there was little difference in signal change
between sequential scans. Parameters were calculated using the Sednterp web server
(Biomolecular Interaction Technologies Center). Data was analyzed using Sedfit (Schuck et al.
2002) and the best-fit molecular weight was calculated for a single species.
118
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couples endoplasmic reticulum load to secretory capacity by processing the XBP-1
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selective bZIP-binding peptides. (2009). Nature 458:859–864.
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proteins HBZ, MEQ, BZLF1, and K-bZIP using coiled-coil arrays. (2010). Biochemistry
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Chapter 4
Conclusions and future directions
121
In this thesis, coiled-coil anti-bZIP sequences were designed in a modular fashion to
target the dimerization domain of human bZIP transcription factors. Below, I summarize the
different methods used and information learned from them. I then present a new design protocol
that utilizes information gained from both studies and suggest one way the designed peptides and
new scoring function can be used to learn more about bZIP function.
Summary of design methods
Surface-core modularity
In Chapter 2, I expanded upon the Grigoryan et al. study to characterize the affinities of
anti-bZIP peptides for their bZIP targets and optimized the affinities of these complexes. The
designed sequences were initially generated by optimizing the core a, d, e, and g positions of the
peptide for target specificity and affinity. The surface b, c, and f positions were chosen to
complement the core residues and keep the final sequence properties similar to native coiled-coil
sequences (Grigoryan et al. 2009). I used a solution FRET assay to determine equilibrium
dissociation constants for the original designed peptide and bZIP target complexes and
redesigned the surface positions in the anti-bZIP peptide to increase the affinity of the designtarget interaction.
Previous coiled-coil design studies have focused primarily on optimizing the core a, d, e,
and g positions for both specificity and affinity of interactions (O’Shea et al. 1993, Vinson et al.
1993, Zhou et al. 1994, Havranek and Harbury 2002, Reinke et al. 2010, Chen et al. 2011).
However, there is precedent for surface residues contributing to dimer affinity. Substituting
residues with high helix propensity into the f positions of the GCN4 dimer stabilized the dimer
relative to wildtype (Zitzewitz et al. 2000). Because binding and folding in coiled coils are
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coupled processes (Thompson et al. 1993), the affinity of this mutant GCN4 homodimer was
stronger than wildtype. Therefore, to increase the affinity of the design-target complex, I
redesigned the surface positions of the anti-bZIP peptide to increase the helical content. New
residues for positions b, c, and f were chosen to increase helix propensity of the sequence and to
introduce favorable positive and negative charge residue patterning to facilitate potential intrahelical salt-bridge formation.
The experiments indicated that surface redesign did increase the design-target affinity
from 3-fold, in the case of OPTanti-FOS for FOS, to at least 90-fold in the case of OPTantiXBP1_A for XBP1. However, due to introducing more charged residues, the specificity of some
peptides was dramatically changed. Both OPTanti-XBP1_A and OPTanti-XBP1_B interacted
more tightly with XBP1 than the original anti-XBP1 peptide. However, the specificity profiles
between the designs differed significantly, and OPTanti-XBP1_B was not specific for XBP1.
Performing experiments in high salt caused the specificity profiles of OPTanti-XBP1_A and
OPTanti-XBP1_B to converge (Figure 2.4c), suggesting the larger number of charged residues in
OPTanti-XBP1_B was influencing the specificity profile. Mutating four of the five residues that
differed between OPTanti-XBP1_B and OPTanti-XBP1_A from glutamic acid to glutamine had
the same effect on interactions as high-salt conditions. Moreover, the specificity profile of the
glutamine mutant was very similar to the profile of OPTanti-XBP1_A (Figure 2.5b), suggesting
the negative charges in OPTanti-XBP1_B were responsible for the differences in specificity.
Analyses of sequences suggested long-range electrostatics could be partially responsible for the
off-target bZIP interactions made by OPTanti-XBP1_B. However, ambiguity in the exact
structures formed by OPTanti-XBP1_B and off-target bZIPs prevented a more thorough
investigation of the exact mechanisms causing the different interaction profiles.
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Heptad assembly
In Chapter 3, designed peptides were generated by concatenating native heptads into a
full coiled-coil peptide. Using a new scoring function developed by Vladimir Potapov and
derived entirely from experimental data, each heptad in the sequence was predicted to bind more
tightly to its corresponding heptad in the target bZIP than to corresponding heptads in off-target
bZIPs. This process generated four highly specific and tightly binding anti-bZIP peptides
targeting JUN, ATF4, ATF5, and XBP1.
Generating the designed sequences from a library of native heptads eliminated the need
to use a conditional probability to choose complementary surface residues for the fixed core
positions, as was done in Chapter 2 and in the original Grigoryan et al. study. Although the
scoring function only considered residue interactions between core positions, the full-length
heptad was used, including its surface residues.
A few reasons may explain why the peptides generated with heptad assembly interacted
more tightly and specifically with their targets than the peptides generated using surface-core
modularity. First, the peptides in the two studies were generated using different scoring
functions. The quantitative information from the Reinke et al. study provided over 5,000 data
points of interacting bZIPs with associated affinities (Reinke et al. 2013). These data were used
to train a machine-learning model to estimate contributions to dimer affinity of different residue
pairs and triplets. Comparison of this function to previously developed scoring functions
indicated that this machine-learning model gives better prediction performance (Table 3.1).
Second, staying entirely within native heptad space eliminated the need to introduce new surface
residues that could influence the interactions of the core residues. These heptads have evolved
124
over time for specificity and affinity of interactions with native partners, and their surface
residues are compatible with their core residues.
Combining heptad assembly and surface-core modularity
In Chapter 2 I showed that introducing sequence properties that favored helix formation
in the anti-bZIP peptides increased the affinity of designed peptides for their targets. This
approach can also be incorporated into the heptad assembly technique. In the method
implemented in Chapter 3, an optimization protocol was used for selecting the best heptads, with
filters and constraints placed on selected heptads to eliminate undesirable heptads and potentially
destabilizing interactions. I propose three additions to the design protocol, listed below.
(1) Because we know that favoring more helical sequences increases binding affinity for
the target, the design process could preferentially incorporate heptads with total larger helical
propensities. The heptad library used to generate the designed peptides contained 1,303 heptads,
but there were only 660 unique combinations of core residues. The remaining 643 heptads had
one of the combinations of core residues but different surface residues. These heptads with
identical cores but different surface residues were important when determining if heptads could
be joined together because residues at the junctions were checked to see if they were found in
native sequences. If multiple heptads with identical core residues but different surface residues
have good junction scores, the design process can calculate which heptad is predicted to favor
helix formation and incorporate that heptad into the final design sequence. Total helical
propensities of the heptads can be calculated by adding the individual residue propensities
determined by O’Neil and DeGrado (O’Neil and DeGrado 1990).
(2) Another way to potentially increase helicity in the designed peptides is to add a helix-
125
capping motif to the peptide’s termini. Helix-capping describes the alternative ways helices can
satisfy the N- and C-terminal residues that lack hydrogen-bond partners for their amide hydrogen
and carbonyl group, respectively (Aurora and Rose 1998). At the N-terminus, a capping motif
includes residues with hydrogen-bond acceptors in their side chains for the free amide hydrogen
atoms. Introduction of the N-terminal motif S-X-X-E into GCN4 by mutation of the first and
fourth residues increased both the helical signal of GCN4, as determined by CD, and the stability
by 0.5 kcal/mole/monomer relative to reference alanine residues. A crystal structure of this
mutant confirmed that the N-terminal residues are more structured than in wild-type GCN4 (Lu
et al. 1999). This GCN4 mutant introduced the serine and glutamate at g and c positions,
respectively, but the designed sequences generated in Chapters 2 and 3 all began at f positions.
Therefore, incorporation of S-X-X-E would involve mutating the first f and b positions.
I introduced this N-terminal motif and the motif X-Pro (Ccap-C’) at the C-terminus into
some of the original anti-bZIP peptides in an early attempt to increase the helicity of the
designed peptides. Statistical analysis of the protein database indicated that asparagine-proline
appears more frequently in proteins than expected (Prieto and Serrano 1997). Additionally,
asparagine as the Ccap residue was shown to be more stabilizing at that position than glutamine,
serine, alanine, glycine, and threonine (Lyu et al. 1993). Figure 4.1 shows CD wavelength scans
and thermal melts for both the original anti-LMAF-3 peptide and the capped version. In the
wavelength scan, the magnitude of the CD signal at 222 nm actually decreased for CAPantiLMAF-3 compared to anti-LMAF-3. A few groups use the ratio of the CD signal at 222 nm to
208 nm as a measure of a folded coiled coil, with a ratio above 1.0 indicative of a fully folded
coiled coil (Lau et al. 1984, Engel 1991, and Tripet et al. 2000). These ratios are .91 and .92 for
126
anti-LMAF-3 and CAPanti-LMAF-3, respectively, which suggests a slightly stabilized design
homodimer. Comparison of the thermal stabilities of anti-LMAF-3 and CAPanti-LMAF-3
clearly showed the CAPanti-LMAF-3 dimer was more thermally stable, as the thermal stability
of the capped dimer increased by 12 °C (Figure 4.1b). This suggests capping motifs can stabilize
the anti-bZIP peptides.
Figure 4.1
(a)
(b)
Figure 4.1 CD wavelength scan and thermal melt for the original and capped anti-LMAF-3 peptides. (a)
Scans were performed at 25 °C with 20 µM peptide in 1x PBS + 1 mM DTT. See Chapter 2 for Method details.
(b) The signal was monitored at 222 nm with 2 µM peptide. The melting temperatures were 45.7 and 57.7 °C for
anti-LMAF-3 and CAPanti-LMAF-3, respectively.
(3) In the design protocol in Chapter 3, filters and constraints were placed on final
heptads incorporated into the designed peptide. These filters and constraints, for example,
eliminated heptads with uncommon residues at the important a and d positions and prevented
potentially destabilizing interactions between residues in the designed peptide and target (see
Methods in Chapter 3 for details). An additional filter was used to keep native N-terminal
heptads at the N-terminus of the designed peptides. However, looking at the residue distributions
of amino acids in N-terminal, C-terminal, and middle heptads, it is clear that C-terminal heptads
also differ from middle heptads (Tables 4.1 and 4.2). A filter to keep native C-terminal heptads
127
only at the C-terminus of the designed peptide can be added to the design protocol. This would
prevent a residue like tyrosine, which is very common at the a position of C-terminal heptads,
from appearing in the middle of a designed peptide where tyrosine at a is less common.
Including this filter for the peptides generated in Chapter 3 could have prevented including
tyrosine at the a position in anti-XBP1b, which mutational analysis showed did contribute to a
weak interaction with XBP1. Additionally, from the residue frequencies listed in Tables 4.1 and
4.2, it appears that residues common in the C-terminal heptad would help to stabilize the Cterminus. Positional preferences for each amino acid within an alpha helix have been determined
(Richardson and Richardson 1988). Lysine and arginine are preferred at the C-terminus of
helices because their side chains can act as hydrogen-bond donors, and these residues are also
more prevalent at the d positions of C-terminal heptads.
More studies would be needed to determine if incorporating the most helical heptad that
still has a good junction score and helix-capping motifs would improve the affinities of the
designed peptides for their targets.
Table 4.1
Table 4.1 Frequency of appearance of a given amino acid at the d position in the N-terminal, middle, or Cterminal heptad. Data are from 275 native bZIP sequences (Vladimir Potapov, unpublished results).
128
Table 4.2
Table 4.2 Frequency of appearance of a given amino acid at the a position in the N-terminal, middle, or Cterminal heptad. Data are from 275 native bZIP sequences (Vladimir Potapov, unpublished results).
Anti-bZIPs as reagents for understanding bZIP function
To be an effective inhibitor, an engineered peptide must bind tightly and specifically to
its target. Specific binding eliminates ambiguity when interpreting the results. Tight binding
ensures the reagent can compete with other high-affinity native binding partners. In this thesis,
tight and specific peptides were characterized targeting CREBZF, FOS, ATF6, XBP1, JUN,
ATF4, and ATF5.
There is not a lot of information known about the genes and processes regulated by
CREBZF. Recently, CREBZF was shown to stabilize and enhance p53 transcriptional activity
(Lopez-Mateo et al. 2012). Genetic evidence indicating a possible protective role for CREBZF
includes linkage analysis showing that a mutated crebzf locus in some prostate cancers
predisposes men to a more aggressive form of the disease (Schaid et al. 2006) and loss of the
locus is commonly seen in many malignant melanoma cell lines (Jonsson et al. 2007). These data
suggest CREBZF may have tumor suppressor activity, but further characterization regarding how
129
CREBZF regulates p53 activity and whether loss or mutation of CREBZF contributes to
oncogenic transformation of cells is needed.
Transfection experiments with anti-CREBZF could reveal whether loss of CREBZF
contributes to oncogenic transformation. Using the designed anti-CREBZF peptide as an
inhibitor would potentially work better than RNAi knockdown of CREBZF because alignment of
the coding regions of CREBZF and the closely related ATF6B gets a score of 67.9 by ClustalW
(Larkin et al. 2007). Several mismatches between a siRNA molecule and mRNA sequence can
be tolerated (Snove and Holen 2004), and the high similarity of CREBZF to ATF6B may cause
off-target effects if the siRNA targets a similar region and the genes are co-expressed.
It is possible the anti-CREBZF peptide could interact with other coiled coils within the
cell. The specificity profile in Chapter 2 was limited to other native bZIP proteins, but many
coiled coils are predicted within the genome (Rose et al. 2005) that could act as off-targets for
the designed peptide. However, adding Vinson’s acidic extension to the N-terminus would favor
the design-target interaction. This tag was developed to complement the basic region and extend
the coiled-coil interface (Krylov et al. 1995), and other coiled coils within the cell unrelated to
bZIPs would be less likely to have a domain similar to the basic region.
There is precedent for transfection of peptides targeting bZIP proteins (Olive et al. 1997
and Ham et al. 1995). Using our designed anti-bZIP peptides would enable specific targeting of
certain oncogenic bZIPs. However, transfection of the anti-bZIP peptide would eliminate all
interactions the target bZIP makes. In the case of CREBZF, this would preclude knowing which
bZIP-mediated interactions are causing its protective functions. CREBZF forms a strong
homodimer and heterodimer with the bZIPs XBP1 and ATF6B in vitro (Reinke et al. 2013). To
determine if the interaction of CREBZF with itself or other bZIPs is regulating genes that protect
130
the cell, the scoring function developed in Chapter 3 can be used to predict selective mutants of
CREBZF that prevent it from interacting with one partner. For example, three mutants of
CREBZF can be designed: CREBZFa, CREBZFb, and CREBZFc. CREBZFa would be predicted
to interact with XBP1 and ATF6B but not to homodimerize. CREBZFb would be predicted to
homodimerize and heterodimerize with XBP1 but not ATF6B. CREBZFc would be predicted to
homodimerize and heterodimerize with ATF6B but not XBP1. Cloning these mutant CREBZF
zipper domains as replacements of the wild-type CREBZF leucine zipper, and transfecting the
construct into a cell with wild-type crebzf deleted, could potentially reveal the specific
interactions CREBZF makes that protect the cell. For example, if transfection of mutant
CREBZFb causes oncogenic transformation, it would suggest the interaction between CREBZF
and ATF6B has tumor suppressor activity because interactions between CREBZFb and itself and
with XBP1 would still be functional. Combining the anti-bZIP peptides with selective bZIP
mutants to target specific bZIP interactions has the potential to provide insight into new
mechanisms or gene products that could potentially be targeted by therapeutics.
131
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Appendix A
Characterization of original designed anti-bZIP peptides
anti-LMAF, anti-LMAF-3, anti-JUN, anti-CREB-3, and
anti-CREBZF-2
134
This appendix is an overview of other original anti-bZIP peptides from Grigoryan et al.
2009 that were tested using the solution FRET assay described in Chapter 2. Different designs
targeting oncogenic bZIPs were chosen for solution testing to determine whether they could
stably and specifically interact with a particular target. Anti-JUN, anti-ZF-2 (anti-CREBZF-2),
anti-LMAF, anti-LMAF-3, and anti-CREB-3 were chosen for testing (Table A.1).
Table A.1
Table A.1 Sequences of the intended targets and designs that were tested. In parenthesis is the oncogenic offtarget bZIP that was shown to interact stably with the designed peptide on the coiled-coil array in Grigoryan et al.
2009.
The anti-LMAFs and anti-JUN peptides target the large MAF family and the JUN family,
respectively, both of which are members of the AP-1 family of transcription factors. The JUN
proteins regulate cell proliferation (Shaulian et al. 2002). The MAF proteins are involved in
early tissue specification and later terminal differentiation. Members of both families cause
oncogenic transformation when mis-regulated (Eychene et al. 2008). Anti-CREB-3 was tested
because the fluorescence signal from the array indicated this peptide interacts stably with FOS
(Grigoryan et al. 2009), another member of the AP-1 family that activates gene expression by
forming heterodimers with other bZIPs (Ransome and Verma 1990). Anti-CREBZF-2 was tested
135
because it formed a stable interaction on the array with XBP1, a key factor in the unfolded
protein response and a regulator of B-cell differentiation (He et al. 2010).
Characterization of the designs by FRET
The C-terminal acceptor-labeled designed peptides were tested against a panel of 31 Cterminal donor-labeled human bZIPs using the FRET assay. Protein purification, labeling with
fluorophores, and the FRET assay were performed and results analyzed as described in Chapter
2. Equilibrium dissociation constants (Kd’s) were calculated at 37, 23, and 4 ºC for all
interactions tested. Table A.2 lists the affinities of the designed peptides for their targets at 37
°C. All interactions tested are listed in tables A.3 through A.7 at the end of this appendix. AntiLMAF, anti-LMAF-3, anti-JUN, and anti-CREBZF-2 did bind their targets at 37 ºC with
affinities of 376 nM, 5 nM, 481 nM, and 1 nM, respectively. Anti-CREB-3 did not bind
detectably to CREB1 up to 1 µM of acceptor, but the peptide did bind to FOS with a Kd of 6 nM.
Table A.2
Table A.2 Calculated affinities of design-target interactions. A “*” indicates the target was not the original
intended bZIP target but was an off-target interaction shown to be stable on the array (Grigoryan et al. 2009).
Anti-LMAF was not specific for its target MAF and did not bind detectably with the
related MAFB at 37 °C (Table A.3). Anti-LMAF-3 did bind tightly and specifically to MAF
(Table A.4). The closest off-target competitor was CREB3L3, with an affinity of 10.6 nM at 37
136
°C, making anti-LMAF-3 ~2-fold specific for MAF (10.6/4.6). Anti-JUN interacted weakly with
JUN at 37 °C and was not specific (Table A.5). Tighter binding was detected between anti-JUN
and NFE2L3 (Kd =168 nM). Both anti-CREBZF-2 and anti-CREB-3 were studied because the
array indicated these peptides interact stably with XBP1 and FOS, respectively (Grigoryan et al.
2009). The solution data agreed with this. Anti-CREBZF-2 did bind its intended target CREBZF
with an affinity of 1.1 nM, but it interacted more tightly with XBP1, with an affinity < 1 nM at
37 °C. However, interactions made by anti-CREBZF-2 with CREB3L1 and CREB3L3 also had
affinities < 1 nM (Table A.6). Testing the interactions in 3 M urea to accurately quantify
affinities between anti-CREBZF-2 and XBP1, CREB3L3, and CREB3L3 could establish if antiCREBZF-2 is specific for XBP1. Anti-CREB-3 did not bind detectably to CREB1, but it did
bind to FOS with an affinity of 6 nM. However, anti-CREB-3 interacted with NFE2L1 with an
affinity of 2.3 nM, indicating this peptide was not specific for FOS (Table A.7).
Comparison to the array
Comparison of the interactions detected at 23 °C to the original array experiment
(Grigoryan et al. 2009) gives an idea of how reproducible the data are given two different
experiments. Chapter 2 lists the main differences between the FRET and array assays but briefly
they are (1) printing the molecules onto the glass slide in 6 M guanidine hydrochloride likely
presented the human bZIPs as monomers on the surface, providing the array with an advantage
over the FRET assay because many of the bZIPs form homodimers in the conditions used in the
FRET assay (Reinke et al. 2013). (2) In the array assay, labeling with fluorophores and printing
onto the glass slide occurred nonspecifically at primary amines, which could potentially disrupt
interactions. However, the labeling strategies for the FRET assay were designed to minimally
137
interfere with binding. (3) The array assay measurements were performed in 1 M guanidine
hydrochloride, which could weaken some of the designed peptide’s interactions due to shielding
of e-g’ electrostatic interactions in the coiled-coil core under high-salt conditions (Krylov et al.
1998).
However, even with these differences, there is still 70% agreement between all
interactions tested in both assays. Twenty-two percent of the interactions tested occurred only in
the FRET assay, and 8% occurred only on the array. Of the ten interactions detected only on the
array, seven were between a designed peptide and a bZIP that strongly homodimerized (Kd < 250
nM according to Reinke et al. 2013), which is consistent with the array providing an advantage
for detecting a designed peptide’s interaction with a strongly homodimerizing bZIP.
Twenty-seven interactions were detected by FRET and not on the array. Figure A.1
breaks down these 27 interactions between the five designed peptides tested and indicates the
strength of the interactions. The FRET assay is a more sensitive assay than the array (Reinke et
al. 2013), and as mentioned, the labeling strategies used for the FRET assay were designed to
minimally interfere with binding (see Chapter 2). The two designed peptides with the most lysine
residues, anti-CREB-3 and anti-CREBZF-2, contributed 13 of the 27 interactions detected only
by FRET, while the two peptides with the least number of lysine residues, anti-LMAF and antiJUN, contributed six. This supports the possibility that the labeling and surface attachment
strategies used for the array assay in the Grigoryan et al. study potentially interfered with
interactions the peptides made.
None of the peptides tested in this appendix interacted tightly and specifically with the
oncogenic target of interest; therefore, they were either not further analyzed or new peptides
were generated by the methods in Chapters 2 and 3 to bind the given target.
138
Figure A.1
Figure A.1 Interactions detected in the FRET assay and not on the array. The 27 interactions not detected on
the array, binned by design and strength of the interaction.
Additional tables
Table A.3
Table A.3 All calculated Kd’s for anti-LMAF at 37, 23, and 4 °C. Kd in nanomolar.
139
Table A.4
Table A.4 All calculated Kd’s for anti-LMAF-3 at 37, 23, and 4 °C. Kd in nanomolar.
Table A.5
Table A.5 All calculated Kd’s for anti-JUN at 37, 23, and 4 °C. Kd in nanomolar.
140
Table A.6
Table A.6 All calculated Kd’s for anti-CREBZF-2 at 37, 23, and 4 °C. Kd in nanomolar.
Table A.7
Table A.7 All calculated Kd’s for anti-CREB-3 at 37, 23, and 4 °C. Kd in nanomolar.
141
References
Eychene, A, Rocques N, and Pouponnot C. A new MAFia in cancer. (2008). Nature Reviews
Cancer 8:683-693.
Grigoryan G, Reinke AW, and Keating AE. Design of protein-interaction specificity gives
selective bZIP-binding peptides. (2009). Nature 458:859–864.
He Y, Sun S, Sha H, Liu Z, Yang L, Xue Z, Chen H, and Qi L. Emerging roles for XBP1, a
super transcription factor (2010). Gene Expression 15: 13-25.
Krylov D, Barchi, J, and Vinson C. Inter-helical interactions in the leucine zipper coiled coil
dimer: pH and salt dependence of coupling energy between charged amino acids. (1998).
Journal of Molecular Biology 279: 959-972.
Ransome LJ and Verma IM. Nuclear proto-oncogenes FOS and JUN. (1990). Annual Reviews in
Cell Biology 6:539-557.
Reinke AW, Baek J, Ashenberg O, and Keating AE. Networks of bZIP protein-protein
interactions diversified over a billion years of evolution. (2013). Science 340: 730-734.
Shaulian E and Karin M. AP-1 as a regulator of cell life and death. (2002). Nature Cell Biology
4:E131-E136.
142
Appendix B
Characterization of redesigned peptides OPTanti-LMAF
and OPTanti-LMAF-3
143
This appendix is an overview of two additional surface-redesigned peptides that were not
included in the study in Chapter 2. Anti-LMAF and anti-LMAF-3 are two designed peptides
from the original anti-bZIP study targeting the large MAF family of bZIPs (Grigoryan et al.
2009). Anti-LMAF and anti-LMAF-3 interacted with their target MAF at 37 ºC with affinities of
376 nM and 5 nM, respectively, and with the related MAFB with affinities of >5000 nM and 75
nM, respectively, as shown in Appendix I, Table A.2. Members of the large MAF family are
necessary for tissue differentiation and are important factors in multiple myeloma (Eychene et al.
2008). Therefore, we chose to redesign the surface residues of the designed peptides to try and
increase the affinity of these peptides for the MAF family.
Characterization of redesigned peptides targeting the large MAFs
Table B.1 shows the sequences for both the original and surface-redesigned peptides for
anti-LMAF and anti-LMAF-3. The redesign protocol is described in Chapter 2. There are 7 and 9
residue differences between the original and redesigned versions of anti-LMAF and anti-LMAF3, respectively. This changed the formal net charge of the anti-LMAF peptide from -2 to -1, and
from 0 to +5 for anti-LMAF-3. Using the FRET assay described in Chapter 2, the specificity
profiles of OPTanti-LMAF and OPTanti-LMAF-3 were generated at 37, 23, and 4 °C. Table B.2
lists the affinities of the redesigned peptides for the target MAF family. All affinities of tested
interactions are listed in Tables B.3 and B.4 at the end of this appendix.
The redesign process increased the affinity of anti-LMAF for both MAF and MAFB. The
interaction of OPTanti-LMAF with MAF was ~10-fold tighter, from 376 nM to 35 nM, and the
interaction with MAFB was at least 50-fold tighter, from >50000 nM to 115 nM. However like
anti-LMAF, OPTanti-LMAF was not specific for the MAF family. The tightest interaction
144
OPTanti-LMAF made was with CREB3, which increased in affinity at least 227-fold to a final
Kd of 22 nM (Table B.3).
Table B.1
Table B.1 Sequences for the original and surface redesigned-peptides targeting MAF. Sequence differences
between the original and redesigned peptides are denoted in red.
Table B.2
Table B.2 Affinities of redesigned anti-LMAF peptides for the target MAF family.
Of the 58 interactions tested at 23°C with both anti-LMAF and OPTanti-LMAF, 20
changed more than 2.5-fold. All of these interactions were stronger with OPTanti-LMAF, but
they were not all strengthened by the same factor. The affinities became 4-fold to 227-fold
stronger. Figure B.1 compares the affinities of the 20 interactions that changed more than 2.5fold.
One of the interactions that tightened was the design homodimer interaction. The affinity
changed from >1000 nM to 39 nM, at least a 26-fold difference. One possible reason for the
stronger homodimeric affinity is the removal of repulsive inter-helical electrostatic interactions.
A helical-wheel diagram of the designed peptide homodimer indicated that the original designed
peptide forms many favorable electrostatic interactions in the anti-parallel state and four
145
repulsive interactions between the g and g’ positions, as shown in Figure B.2a. In the surfaceredesigned peptide, the repulsive salt bridges between core residues are possibly eliminated by
the formation of i-i+4 intra-helical salt bridges (Figure B.2b). Removal of these repulsive
interactions could be responsible for the large increase in homodimeric affinity.
Figure B.1
Figure B.1 Comparing the affinities of interactions made by ant-LMAF and OPTanti-LMAF at 23 °C.
Interactions shown above differed in affinity by more than 2.5-fold.
Figure B.2
(a)
(b)
Figure B.2 Helical-wheel diagrams of the original and surface-redesigned anti-LMAF homodimeric
interaction. (a) Original anti-LMAF as an anti-parallel homodimer. Favorable inter-helical interactions are in blue,
unfavorable interactions are in red. (b) OPTanti-LMAF as an anti-parallel homodimer with potential intra-helical
interactions shown in green that eliminate unfavorable inter-helical interactions.
146
Neither analyses of helical-wheel diagrams nor sequences revealed any obvious reasons
for why some bZIPs interacted more tightly with OPTanti-LMAF than with anti-LMAF. Unlike
the optimized anti-XBP1 designs discussed in Chapter 2, there was not a large introduction of
similarly charged residues into OPTanti-LMAF that was likely affecting the specificity. In this
case, more subtle coupling of the new residues with the core residues may be contributing to the
differences in specificity profile.
In the case of OPTanti-LMAF-3, the surface redesign actually weakened the interaction
with the target MAF family (Table B.2). The interaction between OPTanti-LMAF-3 and MAF at
37 °C weakened ~4-fold, from 5 nM to 27 nM. The interaction with MAFB weakened ~1.5-fold,
from 77 nM to 122 nM, but this difference was within experimental error. Additionally,
OPTanti-LMAF-3 was no longer specific for the MAF family (Table B.4). The original antiLMAF-3 was weakly specific. The closest off-target competitor was CREB3L3, with an affinity
of 10.6 nM, making the specificity ratio ~2. OPTanti-LMAF-3 interacted most tightly with itself,
with an affinity of 10 nM.
Of the 58 interactions tested at 23 °C with both anti-LMAF-3 and OPTanti-LMAF-3, 42
changed. Five of the 21 interactions became 3-fold to at least 167-fold stronger. The rest became
4-fold to at least 500-fold weaker. Figure B.3 compares the interactions that changed more than
2.5-fold between anti-LMAF-3 and OPTanti-LMAF-3. One potential reason many interactions
became weaker was the stronger homodimeric interaction made by OPTanti-LMAF-3, which
strengthened 6-fold to a final Kd of 5 nM at 23 °C. A stronger design homodimer will make
heterodimeric interactions harder to detect; however, five interactions did get stronger. Like in
the case of the OPTanti-LMAF homodimeric interaction, replacement of two unfavorable
interactions between core residues with favorable intra-helical salt bridges could potentially
147
explain why the interaction between ATF4 and OPTanti-LMAF-3 was strengthened. Figure B.4
shows the helical-wheel diagrams of the anti-LMAF-3 designed peptides interacting with ATF4.
A similar explanation can be applied to the interaction between OPTanti-LMAF-3 and BATF3.
In the case of CEBPG, the formal net charge of the CEBPG coiled coil is -5. Like in the case of
many interactions OPTanti-XBP1_B made in Chapter 2, it is possible the surface redesign
influenced long-range electrostatic interactions between CEBPG and OPTanti-LMAF-3, which
has a formal net charge of +5.
Figure B.3
Figure B.3 Comparing the affinities of interactions made by ant-LMAF-3 and OPTanti-LMAF-3 at 23 °C.
Interactions shown above differed in affinity by more than 2.5-fold.
Figure B.4
(a)
(b)
Figure B.4 Helical-wheel diagrams of the original and surface-redesigned anti-LMAF3 interaction with
ATF4. (a) Original anti-LMAF-3 interaction with ATF4. Favorable inter-helical interactions are in blue,
unfavorable interactions are in red. (b) OPTanti-LMAF-3 interaction with ATF4. Potential intra-helical interactions
that eliminate unfavorable inter-helical interactions are shown in green.
148
Like the other surface-redesigned peptides, there was not a global change in specificity
that could explain the different profiles for anti-LMAF-3 and OPTanti-LMAF-3. Many factors,
including a stronger design homodimer, change in net charge, and introduction of intra-helical
interactions that compete with core interactions could be contributing to why interactions are
weakened or strengthened. However, this data, combined with the study in Chapter 2, does seem
to indicate that surface residues can influence interactions, including strengthening or weakening
the intended interaction.
Table B.3
Table B.3 All calculated Kd’s for OPTanti-LMAF at 37, 23, and 4 °C. Kd in nanomolar.
149
Table B.4
Table B.4 All calculated Kd’s for OPTanti-LMAF-3 at 37, 23, and 4 °C. Kd in nanomolar.
150
References
Eychene, A, Rocques N, and Pouponnot C. A new MAFia in cancer. (2008). Nature Reviews
Cancer 8:683-693.
Grigoryan G, Reinke AW, and Keating AE. Design of protein-interaction specificity gives
selective bZIP-binding peptides. (2009). Nature 458:859–864.
151
Appendix C
Characterization of stapled and unstapled anti-bZIP
peptides
Collaborator notes:
All of the chemically-synthesized peptides were made by Dr. Jason Marineau at
Dana Farber Cancer Institute.
152
This appendix is an overview of a different method I used to try and increase the affinity of
the anti-bZIP-bZIP interaction. In Chapter 2, residues with larger helical propensity and the
ability to form favorable intra-helical side-chain interactions were introduced into the anti-bZIP
peptides to increase affinities of designed peptides for target bZIPs. Another technique used by
other groups to increase helical content is the incorporation of a cross-link into the peptide to
constrain conformations. If certain non-natural amino acids are placed in the i and i+4 or i+7
positions, the side chains can be cross-linked together along one or two turns of the helix,
respectively, leaving the linkage along one helical surface (Bernal et al. 2007, Moellering et al.
2009, Judice et al. 1997, Sia et al. 2002, Stewart et al. 2010, Walensky et al. 2006, and Zhang et
al. 2008). In all cases, these cross-linked peptides were shown to be more helical in solution by
CD.
We collaborated with James Bradner’s lab at Dana Farber Cancer Institute to incorporate a
specific linkage, the hydrophobic staple, into the anti-bZIP peptide (Figure C.1). The non-natural
amino acids are incorporated during chemical synthesis, and shorter sequences are easier to
synthesize. Therefore, I first began by testing different truncated versions of designed peptides
from Grigoryan et al. 2009 for interaction with a bZIP target. I also tested truncated forms of
native bZIP leucine-zipper domains, as the affinities of the designed peptides for their targets
were unknown at the time and native peptides would likely have high affinities for native binding
partners. The goal was to find a sequence less than 30 amino acids that could still detectably
interact with a target bZIP by CD, as this was before the solution FRET assay was developed.
Table C.1 lists the sequences of the truncated peptides tested and the bZIPs they targeted. Only
the peptide listed in red, aFT1, still maintained an interaction with its target that was detectable
153
by CD (Figure C.2).
Figure C.1
(a)
(b)
Figure C.1 Structure of the non-natural amino acid used to form the hydrophobic staple. (a) The non-natural
amino acid used at the i and i+4 or i+7 positions. (b) Structure of the hydrophobic staple. All syntheses were
performed by Jason Marineau in James Bradner’s lab at DFC1.
Table C.1
Table C.1 Sequences of truncated native and designed coiled-coil peptides tested for interaction with fulllength bZIPs.
Figure C.2
Figure C.2 CD thermal melt of aFT1 with FOS. The designed peptide was at 5 µM and FOS was at 1 µM in
buffer 1 x PBS pH 7.4 (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4) + 1 mM DTT. The melt
was monitored at 222 nm.
154
Characterization of the modified-truncated peptides
The non-natural amino acids were placed at b and f positions to prevent disruption of core
interactions. Our collaborators attempted to synthesize stapled and unstapled variants of aFT1.
The “unstapled” form refers to peptides with the non-natural amino-acid substitutions but the
side chains are not cross-linked together. Table C.2 lists the different aFT1 variants.
Table C.2
Table C.2 Three modified versions of aFT1. Non-natural amino acids are denoted with a red “X.” Addition of
“s2,” “s4,” and “s6” to the ID indicates a staple is present. If only the non-natural amino acids are present, the ID
is “u2,” “u4,” or “u6” for unstapled.
Due to the difficulty of the synthesis, only a subset of the peptides were synthesized and
HPLC-purified in both the stapled and unstapled form. All peptides confirmed by MALDI mass
spectrometry after purification were tested.
Two assays were used to determine the effects of the staple or non-natural amino-acid
substitutions. First, a CD wavelength scan was used to determine if the stapled and unstapled
forms of the peptide were more helical in solution than the original, unmodified sequence. Table
C.3 lists the MRE at 222 nm for the sequences tested. The magnitude of the signal for all
modified peptides tested was larger than the signal for aFT1, suggesting that both the staple and
non-natural amino-acid substitutions alone were making the peptide more helical in solution.
155
Table C.3
Table C.3 Calculated MRE at 222 nm for the original and substituted aFT1 peptides. Measurements were
taken at 25 °C with 10 µM peptide in 1x PBS pH 7.4 + 1 mM DTT.
The second assay used was a competition FRET assay, as described in Chapter 2. The
unlabeled peptides were tested to see if they could inhibit a FOS-JUN FRET complex. Figure
C.3 compares the different peptides acting as inhibitors of FOS-JUN dimerization. None of the
peptides were strong inhibitors, and upper baselines for the inhibition curves were not obtained
up to 10 µM of peptide. However, both the stapled and unstapled peptides did act as better
inhibitors than the original aFT1 peptide.
Interestingly, in the two modified cases, the unstapled peptide was a better inhibitor than
the stapled form, although both were very weak. This is contrary to what has been published,
where it is the actual cross-link that increases the potency of binding (Bernal et al 2007). A few
hypotheses might explain why the stapled form weakens the interaction. One possibility is that
the staple causes structural strain when the coiled coil forms. In a dimer, the two helices wrap
around each other into a super coil. Structural strain due to the cross-link could be weakening this
super coil and destabilizing the stapled peptide’s interaction with FOS. Another possibility is
that the staple is not causing structural strain but is instead greatly increasing the homodimeric
stability of the truncated peptide. The full-length anti-FOS peptide is a strong homodimer, with
an affinity of 85 nM at 37 ºC as stated in Chapter 2. Removing two heptads from anti-FOS to
156
generate aFT1 likely weakened the homodimer, as it weakened the heterodimeric interaction
between aFT1 and FOS, but the staple within each truncated peptide may greatly increase the
homodimeric affinity.
Figure C.3
(a)
(b)
Figure C.3 Modified peptides acting as inhibitors of FOS-JUN dimerization. (a) The unstapled peptides
aFT1u2 and aFT1u6 compared to the unmodified aFT1. (b) The stapled peptides aFT1s2 and aFT1s6 compared to
the unmodified aFT1. The assay was performed as described in Chapter 2. Here, the FRET complex is 10 nM FOSFITC with 10 nM JUN-TAMRA.
Characterization of a full-length modified peptide
Because the truncated peptides were so weak and modifications to the synthesis protocol
made syntheses of longer peptides easier, an attempt was made to incorporate a staple into the
full-length anti-FOS peptide to determine whether the staple could improve its affinity for FOS,
which was < 1 nM at 37 ºC as determined in Chapter 2. Table C.4 lists the original and stapled
anti-FOS sequences. Only one modified peptide was synthesized. The non-natural amino acids
were placed in the positions corresponding to the aFT1s2 peptide, which performed best in the
competition FRET assay (Figure C.3b).
Due to a difficult synthesis, a high yield of Santi-FOS was not obtained and only the
substituted, unstapled form was synthesized. Comparison of the CD signal at 222 nm to
157
determine if the unstapled anti-FOS was more helical than anti-FOS was not informative. AntiFos is a strong homodimer and already exhibited a strong helical signal. Both were compared as
inhibitors in the FRET assay (Figure C.4).
Table C.4
Table C.4 Original and modified anti-FOS sequences. The non-natural amino acids are denoted as a red “X.”
Figure C.4
Figure C.4 Unmodified and unstapled anti-FOS designs inhibiting FOS-JUN dimerization. The assay
conditions are described in Chapter 2. Here, the FRET complex is 10 nM FOS-FITC with 10 nM JUN-TAMRA.
Again, the data comparing the unmodified, original peptide to the unstapled peptide was
contrary to what has been published in the literature and what was observed with the truncated
peptides tested in Figure C.3. Although both peptides were strong inhibitors and lower baselines
for fitting purposes were not obtained, the original anti-FOS peptide appeared to be a better
158
inhibitor than the unstapled anti-FOS peptide. In this case, the likely reason behind unstapled
anti-FOS being a weaker inhibitor is the strengthening of the unstapled anti-FOS homodimer.
The non-natural amino acids have a large helical propensity, and incorporation of these residues
into the surface positions likely stabilized the homodimeric interaction.
The data generated from this study suggest the possibility that a hydrophobic staple may
not capable of increasing the affinity of coiled-coil interactions. However, this is not definitive.
Most stapled peptide studies measured a large panel of stapled variants, as not all stapled
positions within a peptide had the same effect on helicity and affinity (Bernal et al. 2007,
Moellering et al. 2009, Stewart et al. 2010, and Walensky et al. 2006). A more thorough
investigation of different stapled positions would be needed to determine that the stapled form is
always weaker than the unstapled form, but it would be interesting to study since a long, rod-like
interface has not yet been targeted using the hydrophobic stapling technique.
159
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160
161