“Proteomics & Bioinformatics”
MBI, Master's Degree Program in Helsinki, Finland
Lecture 5
11 May, 2007
Sophia Kossida, BRF, Academy of Athens, Greece
Esa Pitkänen, Univeristy of Helsinki, Finland
Juho Rousu, University of Helsinki, Finland
Mining proteomes
To identify as many components of the proteome as possible
Mapping of proteomes of various
organisms and tissues
Comparison of protein expression
levels for the detection of disease
biomarkers
How to select proteome?
A proteome is defined by the state of the organism,
tissue, or cell that produces it.
Because these states are constantly changing, so
are the proteomes.
Example of proteomes:
different kind of cells; liver, …
extracellular fluids; blood plasma, urine, CSF…
Applications
Systems biology - understand cell-pathways, network, and
complex interacting.
Biological processes - characterize sub-proteomes such as
protein complexes, cellular machines, organelles
Biomarkers - discovery of disease (serological, urine, other
biological fluids) - diagnostics, treat patients, monitor therapies
Drug targets - evaluate toxicity & other biological or
pharmaceutical parameters associated with drug treatment
Protein Profiling
Measure the expression of a set of proteins in two samples
and compare them - Comparative proteomics
• 2D gel electrophoresis
• Difference gel electrophoresis (DIGE)
• LC-MS/MS using coded affinity tagging
(ICAT, iTrac, SILAC..)
• ProteinChip Array (SELDI analysis)
• Antibody arrays
Laser-Capture Micro dissection, LMC
Technique for selectively sampling certain cells within a tissue
Biopsy
Transfer film
Tissue sample
Tumor
Glass slide
Laser beam
activates film
Selected cells are
transferred
Genomic/proteomic analysis
Modified from “National Cancer Institute”, US National Institutes of Health:
http://www.cancer.gov/cancertopics/understandingcancer/moleculardiagnostics/Slide29
Cells
2D gels, DIGE
High resolving power
Absolute / relative quantity
Coomassie
blue stained
gels
Easily archived for further comparison
Detects some PTMs and alternatives
splices
Low troughput
Silver
stained
Poor detection of large, acidic, basic and
membrane proteins
Only high abundance proteins
DIGE
Proteins are labeled
prior to running the first
dimension with up to
three different
fluorescent cyanide dyes
Allows use of an internal
standard in each gel-to-gel
variation, reduces the
number of gels to be run
Adds 500 Da to the
protein labeled
Additional postelectrophoretic staining
needed
Mix labeled extracts
Internal standard
Human brain proteins
Differences in Expression Level in Thalamus
Control
phosphoglycerate mutase
phosphoglycerate mutase
Alzheimer’s Disease
phosphoglycerate mutase
phosphoglycerate mutase
Example of different expression
LC-MS/MS using coded affinity tagging
Moderate throughput, but can be automated
Detects some low abundance proteins
Most isotope label experiments limited to two
versions –heavy and light isotope, i.e. binary
comparisons only
Poor detection of alternative splices and PTMs
Labeling
Chemical, ICAT, ITRAQ
Chemical modifications to amino acids generally after digestion
Most labels differ by 3-10Da in mass (not complete / interferences)
Compares only 2-8 samples
SILAC Stable isotopes incorporated during cell growth
Must be able to grow cells
Compares 2 or 3 samples
Lys (+8 Da) and Arg (+10 Da)
Ion Current
No labeling of any kind, See everything in the sample not just what gets labeled
Normalization issues, (2 separate runs are compared) Standards needed
Robust and many samples and experimental conditions can be compared
Isotope Coded Affinity Tag (ICAT)
Two protein samples, are labeled with normal
and heavy versions of the same isotope-coded
affinity tag (ICAT) reagent, respectively. The
reagent binds to cysteine residues and carries a
biotin-tag.
A
B
Identification
LC-MS-MS
Samples are mixed,
digested and ICAT-labeled
peptides are recovered via
the biotin tag of the ICAT
reagents by -affinity
chromatography.
Quantification
Drawback: Cysteine containing peptides only
heavy
light
m/z
ICAT
• Label protein samples with heavy and light reagent
• Reagent contains affinity tag and heavy or light isotopes
Chemically reactive group: forms a covalent bond to
the protein or peptide
Isotope-labeled linker: heavy or light, depending on
which isotope is used
Affinity tag: enables the protein or peptide bearing an
ICAT to be isolated by affinity chromatography in a
single step
Modified from http://skop.genetics.wisc.edu/AhnaMassSpecMethodsTheory.ppt#260,11,Mass Spectrometry
Example of an ICAT Reagent
Reactive group: Thiolreactive group will bind to Cys
Biotin Affinity tag:
Binds tightly to
streptavidin-agarose
resin
O
Linker: Heavy version
will have deuteriums at *
Light version will have
hydrogens at *
NH
NH
H
N
*
*
S
O
O
O
O
Modified from http://skop.genetics.wisc.edu/AhnaMassSpecMethodsTheory.ppt#260,11,Mass Spectrometry
*
*
H
N
O
I
Stable-isotope labeling
Aebersold and Mann, Nature, 2004
Isobaric tag reagent
Isobaric tags for relative and absolute quantification
Allows us to compare the relative abundance of proteins from
four different samples in a single mass spectrometry
experiment
Isobaric Tag (Total mass =145 Da)
Peptide reactive group
Reporter
Balance
mass=114 to 117
mass 31 to 28
Gives strong signature ion in
MS/MS
Good b- and y-series
Maintains charge state and ion
masses
Signature ion masses lie in quiet
low mass region
Amine specific
Balances the mass
change of reporter to
maintain a total mass of
145
Neutral loss in MS/MS
iTRAQ
Uses up to 4 tag reagents that bind covalently to the N-terminus of the
peptide and any Lysine side chains at the amine group (global tagging).
Each sample set is digested separately and then mixed with the specific
iTRAQ tag
Samples
mixed
MS
114
31
NHS + peptide
115
30
NHS + peptide
Reporter – Balance - Peptide intact
116
29
NHS + peptide
4 samples identical m/z
117
28
NHS + peptide
114
MS/MS
b
Peptide fragments –equal
115
P
116
117
Modified from “Quantitative Proteomics Using
Isotope Tagging of Peptides” by Kathryn Lilley
E P
T
I
D E
Reporter ions different
y
iTRAQ spectrum
Stable isotope labeling in cell culture
1. Cell
culture
with
normal
Arginine
SILAC
2. Cell
culture
plus
“heavy”
Arginine.
heavy
Combine, digest,
(purification)
LC-MS/MS
light
m/z
Quantify levels from peak ratio
cell culture (in vivo)
amino acid metabolism
Steen & Mann, Nature, 2004
SILAC Example
b051010b02 #6799 RT: 82.06 AV: 1 NL: 4.65E7
T: FTMS + p NSI Full ms [ 300.00-1600.00]
671.8832
100
95
90
85
80
75
70
672.3849
Relative Abundance
65
Ratio
~4:1
60
55
50
45
40
4Da @ +2 ion = 8
Da (Lys)
35
30
672.8869
25
20
675.8907
676.3914
15
10
676.8926
677.3937
673.3886
5
0
673.8906
671.7095
672
673
From presentation by: Nicholas E. Sherman, Ph.D.
http://www.healthsystem.virginia.edu/internet/biomolec/Keck_De
c12_2006.ppt#387,15,Slide 15
674
674.8875
675.3894
675
m/z
676
677
678.4000
678
679
SELDI
Surface Enhanced Laser Desorption Ionization
Ionized proteins are detected
and their mass accurately
determined by Time-of-Flight
Mass Spectrometry
High throughput
Small amounts of sample
More reproducible than 2DE, but lower
resolving power
Applied for the analysis of crude
samples
Process is not standardized
The SELDI-chip
Chemical Surfaces
(Hydrophobic)
(Anionic)
(Cationic)
(Metal Ion)
(Normal Phase)
Biological Surfaces
(PS10 or PS20)
(Antibody - Antigen)
(Receptor - Ligand)
(DNA - Protein)
Antibody arrays
Not discovery based
Must have 1 or 2 specific high affinity antibodies
Very high throughput
Can be highly quantitative - relative and absolute
Can design reagents to detect PTMs, splice forms
Antibody array
Forward phase
Sandwich assay
Detection
with 2nd
Antibody
Reverse phase
Direct assay
Detection with
Labeled Analyte
Detection with
Labeled Antibody
Analyte
Antibody immobilized on glass substrate
Analytes immobilized on glass substrate
Modified from slide; FullMoonBiosystemsInc. (http://www.fullmoonbio.com/Doc/Overview.pdf)
Protein Protein Interactions
From single proteins to systems biology
Protein-Protein Interactions
Proteins “work together” forming multi
complexes to carry out the specific
functions
Identification of interactions
Experimental
Computational
•x-ray crystallography
Genomic data
• NMR spectroscopy
• Phylogenetic profiling
• Mass spectrometry
(Tandem
affinity
purification)
• Gene context
• Gene fusion
• Symmetric evolution
• Immunoprecipitation
•Yeast two-hybrid
• Microarrays
Structural data
• Sequence profile
• 3D structural distance matrix
• Surface patches
• Binding interactions
X-ray crystallography
Crystals hard to obtain
Good for large proteins
Bioinformatics center, University of Copenhagen
Modified from presentation
;http://www.biosys.dk/courses/Previous_courses/Introductory_Bioinformatics/protein_structure.pdf
Nuclear Magnetic Resonance
Multidimensional NMR
NMR Spectroscopy
For proteins in solution
Better for small proteins than large ones
Identification by mass spectrometry
Protein complex
SDS-PAGE
Immunoprecipitate
anti-
Peptide mixture
LC-MS-MS
“shotgun” identification
MALDI-TOF
Immunoprecipitation
Immunoprecipitation of a protein of interest, analyzed by 1D-SDS-PAGE
Electrophoretically transferred to membrane, the membrane is probed with
antibodies suspected as partners of the target protein
SDS-PAGE
Protein complex
anti-
anti-
anti-
Immunoprecipitation
Western blot
anti-
Only detects what one sets out to look for.
Obtaining a suitable antibody is important.
The antibody might immuno-precipitate the
protein successfully, but not when other
interacting proteins are present.
undetected
Yeast Two-Hybrid System
A transcription factor is split into 2 domains and two hybrid proteins
are designed.
One protein of interest (bait) is typically fused to a DNA-binding
domain.
The proteins being screened for interactions with the bait (preys) are
fused to a transcription-activating domain.
An interaction between the bait and a prey will bring these 2 domains
close together which in turn results in the transcription of a reporter
gene.
Bait protein
Binding
Domain
Prey protein
The reporter can be:
essential, in which case the colony dies if no interaction
reversely, the reporter gene can be attached to a green
fluorescent protein
mRNA
Activation
Domain
Promoter Region
Reporter Gene
The rate of false positive is high (estimated > 45%)
Microarray co-expression
Microarray: study the expression of genes as a a function of time, or
following treatment with a drug, …
Co-expression of genes are usually a sign that the two proteins interact.
Expression level
Gene A
Gene B
Time or treatment
Identification of Co-expressed Genes
To determine which genes have similar/correlated expression patterns
– to derive their functional relationships
Data clustering
We can represent each gene as a vector (5, 15, 10, 7, 5, 3)
So a set of expression data can be represented as a collection
of data points in K-dimensional space
Genes with similar expression patterns form data clusters
In silico Prediction of PPI
Phylogenetic Profile
The phylogenetic profile of a
protein is a string that encodes the
presence or absence of the protein
in every sequenced genome
Protein
A
Protein
B
Protein
C
Protein
D
Org 1
1
1
1
1
Org 2
0
1
0
1
Org 3
1
0
1
0
Org 4
1
0
1
1
Conserved presence or absence of a
protein pair suggests functional coupling.
A
Phylogenetic profile (against N genomes):
For each gene X in a target genome: if gene X has a
homolog in genome #i, the ith bit of X’s phylogenetic
profile is “1” otherwise it is “0”
C
In silico Prediction of PPI
Gene Context
Conserved gene neighbourhood suggests position- function coupling
Org 1
Protein A
Org 2
Protein B
A
B
Org 3
Protein C
Org 4
Gene Fusion (Rosetta stone)
Seemly unrelated proteins are sometimes found fused in another
organism
Org 1
Org 2
Though gene-fusion has low
prediction coverage, its
false-positive rate is low
In silico Prediction of PPI
Symmetric Evolution
Interaction positions on different proteins
should co-evolve so as to maintain the
interface.
Look for correlation between sequence
changes at one position and those at another
position in a multiple sequence alignment.
Docking
determination of protein complex
structure from individual protein
structures
Structure- and interaction databases
STRING (EMBL)
BOND (Unleashed Informatics)
DIP (UCLA)
iHOP
STRING
http://string.embl.de
Biomolecular Object
Network Databank
BOND
http://bond.unleashedinformatics.com
Database of Interacting Proteins
The DIP database catalogs
experimentally determined interactions
between proteins. It combines
information from a variety of sources to
create a single, consistent set of proteinprotein interactions.
http://dip.doe-mbi.ucla.edu/
ihop
http://www.ihop-net.org/UniPub/iHOP/
Proteomics in human diseases
Fingerprinting of bladder cancer
Combination of protein extract
Laser
LC
+
+
MALDI-TOF/TOF
Flight tube
+
+
blood/urine
Identification of diagnostic proteomic
patterns
Application of bioinformatics
tools
(feature extraction, classification
algorithms)
Bladder Cancer
Benign
Disease classification
Strategy for Biomarker Discovery
Genomic analysis
Disease vs. Normal
Proteomic analysis
mRNA level
(2D gels / MS)
Discovery
Candidate gene
Validation
in situ hybridization Immunohistochemistry
Large # samples
Small # candidates
Application
Clinical Application
Diagnostic Prognostic
Therapeutic
Proteins as biomarkers
The protein composition may be associated with disease processes in the
organism and thus have potential utility as diagnostic markers.
Proteins are closer to the actual disease process, in most
cases, than parent genes
Proteins are ultimate regulators of cellular function
Most cancer markers are proteins
The vast majority of drug targets are proteins
Individual biomarkers are not sufficient for accurate disease detection
Panel of biomarkers should be established
Benefits of Molecular Diagnostics
proteins
Patient’s
blood sample
MS
Ovarian pattern
• Create new cancer screening tools
• Inform design of new treatments
• Monitor treatment effectiveness
• Predict patient’s response to treatment
From known samples to serum proteins
no cancer
Patterns as screening tool
proteins
MS
Protein patterns
proteins
cancer
MS
Early diagnosis
of disease
Early warning
of toxicity
Proteomics in nutrition of food
Development of fingerprinting techniques to identify changes in
modified organisms at different integration levels (2D gels, MALDI)
MALDI-MS).
Identification of unintended side effects
A proteome analysis of livers from mice traeted with WY14.643
Isolation of protein spots
Peptide mapping
MALDI-TOF analysis
Amino acid sequence
Data base
16 proteins
Liver proteins from control
Protein identified
Proteins from animals after treatment
http://i-council-biomed-biotech.org/Contacts%20to%20Add_files/Haoudi%20Oman%20Feb%202005.pdf
Identification of breast cancer biomarkers by iCAT LC-MS
Biomarker Discovery
•
Markers can be easily found by
comparing protein maps.
•
SELDI is faster and more
reproducible than 2D PAGE.
•
Has been used to discover
protein biomarkers of diseases
such as ovarian cancer, breast
cancer, prostate and bladder
cancers.
Modified from Ciphergen Web Site)
Gene Ontology
A knowledge representation about the word or some part of it.
An ontology is used as a description of the concepts and
relationships that exist for a community of agents.
Ontology generally describes:
• Individuals: the basic or “ground level” objects
• Classes: sets, collections, or types of objects
• Attributes: properties, features, characteristics, or parameters that
objects can have and share
• Relations: ways that objects can be related to one another
from: wikipedia
Goals
Develop a set of controlled, structured vocabularies – gene
ontology (GO) to describe aspects of molecular biology
Describe gene products using vocabulary terms (annotation)
Provide a public resource, allowing access to the GO,
annotations and software tools developed for use with the GO
data
www.geneontology.org
The Three Ontologies
Molecular Function — describes activities, or tasks, performed
by individual or by assembled complexes of gene products.
DNA binding, transcription factor
Biological Process — a series of events accomplished by one
or more ordered assemblies of molecular functions.
NOT a “pathway”!
mitosis, signal transduction, metabolism
Cellular Component — location or complex , a component of a
cell, that also is part of some larger object
nucleus, ribosome, origin recognition complex
Relationships between terms
Directed acyclic graph: each child may have one or more parents
Every path from a node back
to the root must be
biologically accurate (the true
path rule)
Relationship types:
is_a; class-subclass relationship, meaning that a is a type of b
Exemple: nuclear chromosome is_a chromosome.
part_of :physical part of (component) subprocess of (process)
part_of c part_ of d, meaning that whenever c is present, it is a
part of d, but c doesn’t always have to be present.
Example: nuleus part_of cell ; meaning that nuclei are always
part of a cell, but not all cells have nuclei.
Relationships between terms
Example:
the biological process term hexose biosynthesis has two parents, hexose
metabolism and monosaccaride biosynthesis. This is because biosynthesis is a
subtype of metabolism, and a hexose is a subtype of monosaccharide.
When any gene involved in hexose biosynthesis is annotated to this term, it is
automatically annotated to both hexose metabolsim and monosaccharide
biosynthesis, because every GO term must obey the “true path rule”, if the child
term deescribes the gene product, then all its parent terms must also apply to that
gene product..
Evidence codes
IC: Inferred by Curator
IDA: Inferred from Direct Assay
IEA: Inferred from Electronic Annotation
IEP: Inferred from Expression Pattern
IGC: Inferred from Genomic Context
IGI: Inferred from Genetic Interaction
IMP: Inferred from Mutant Phenotype
IPI: Inferred from Physical Interaction
ISS: Inferred from Sequence or Structural Similarity
NAS: Non-traceable Author Statement
ND: No biological Data available
RCA: Inferred from Reviewed Computational Analysis
TAS: Traceable Author Statement
NR: Not Recorded
Gene Ontology Home
GO tools
•search for gene products and view the
terms with which they are associated;
•search or browse the ontology for GO
terms of interest and see term details and
gene product annotations.
•AmiGO also provides a BLAST search
engine, which searches the sequences of
genes and gene products that have been
annotated to a GO term and submitted to
the GO Consortium.
Annotation tools
ReBIL
Gene expression tools