Mapping Protein-Protein Interactions
MEDG 505 (Genome Analysis)
13 January 2005
•Morin:
-Overview
-IP-MS
-Data integration
•Student presentations:
-Y2H interactions
-RNA vs Protein expression analysis
•Discussion:
-Lessons
-Application
Central Dogma
DNA
RNA
Protein
Humans:
- ~25,000 genes
- 25-40% with functional annotations
General Goal: Annotation of proteome
-Identify disease related proteins
-Identify therapeutic targets
How identify protein functions?
Function
Protein Function
General purpose of proteins is to interaction with other molecules
-Enzyme/substrate
-Protein/protein
Cellular processes governed by complex networks of interacting proteins
-Determination of protein-protein interactions infers functional hypotheses
Protein Annotation
Large Scale Methods for annotation of protein function:
-Genetic
-can verify biological role
-Mutational analysis in model organisms
-verifies biological role
-binary interactions
-Yeast 2-hybrid
-comprehensive
and HTP
-translation to humans
-often protein fragments
-Genomic
-mRNAs
infer proteome
problematic
-high false positives
-mRNA profiling
-identifies
expression
-differences
in biologychanges
cloud
-identifies
interactions
-extensively
employed directly
-Biochemical
-silent
to
PTMsorder interactions
interpretation
-yields
higher
-MS analysis of purified protein complexes
-cause
and PTMs
effect difficult to infer
-identifies
-interactions
difficult
to employed
predict
-binding affinity
can be
-technically
challenging
Lesson: All methods need to be employed to fully annotate
proteome.
IP-MS
Immunoprecipitation Mass Spectrometry
Immunoprecipitate Interaction Partners
Protein identification
Gel
separation
Excise
bands
LC-MS/MS
fragmentation
Tagged Protein Structure
N-tagged construct
CMV
FLAG lox
lox
ORF
C-tagged construct
CMV
lox
ORF
lox
FLAG
Properties of Immunoprecipitated
Protein Complexes
Types of interacting proteins
• Background binding to bait/matrix/MS (filter?)
• Proteins from throughout lifespan
• Processing/transport/degradation proteins (filter?)
• Weak affinity (less reproducible?)
• Strong affinity
• Primary interactors
• Secondary interactors
• High data volume
Experimental design and analysis
should be designed for expectations
Methodology for evaluation
1-Experimental validation
2-Bioinformatic evaluation
3-Experimental reproducibility
-transfection/IP protocols
Method Characterization
Characterization Project
1- 49 Baits, from diverse protein families
-tag both N and C termini
2- IP-MS, repeat 4+ times
3- 190 preys
-hit:
-observed 2+ times
-frequency less than 5%
4- Analyze
N- & C-Tag Hit Overlap
seen with N only
seen with C only
seen in both N&C
seen when N+C are combined
total
# hits
110
29
15
8
162
% of total
hits
0.68
0.18
0.09
0.05
Fraction of total hits
observed
N-tag only experiment
0.77
C-tag only experiment
0.27
Lessons:
1) 5 Hits per Bait.
2) N-tags interfere less than C-tags.
3) Both tags needed to get good representation.
Sample
33 Baits
Prey Reproducibility
Observed Reproducibility Rate
0.39
0.31
0.40
0.30
N
0.17
Average
C
0.04
0.01
0.07
0.10
0.02
0.20
0.01
Fraction of Hits
0.50
0.00
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
Reproducibility Rate
Sample
42 Baits
190 Preys
Note: ~50% of C-tags have 1.0 rate.
Lesson: Improve immunoprecipitation conditions.
Question: How many trials to see a prey 2 times?
Planning Trial Size
Number of Trials Needed to Observe Prey 2+
Times
1
0.9
Fraction of Hit Pool
0.8
# of trials
0.7
2
3
0.6
4
0.5
5
0.4
6
0.3
Reproducibility
Rate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Theoretical Probability of 2+
observations in X # of trials
2
3
4
5
6
0.00 0.00 0.00 0.00 0.00
0.01 0.03 0.05 0.08 0.11
0.04 0.10 0.18 0.26 0.34
0.09 0.22 0.35 0.47 0.58
0.16 0.35 0.52 0.66 0.77
0.25 0.50 0.69 0.81 0.89
0.36 0.65 0.82 0.91 0.96
0.49 0.78 0.92 0.97 0.99
0.64 0.90 0.97 0.99 1.00
0.81 0.97 1.00 1.00 1.00
1.00 1.00 1.00 1.00 1.00
0.2
0.1
0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Reproducibility Rate
Binomial distribution equation
n!
n
Lessons:
(Lessons:
Pobs)  k
( p k )((1  p) nk )
ksuspect
!(n  k )!
•Identifies
•Identifiessuspect
data
data
p: prey observation
frequencyrate
•Improving
•Improving
reproducibility
reproducibility
rate
reduces
number
oftrials
trialsneeded.
needed.
n:reduces
number number
of
trials of
k: number of observations required (2)
Fraction Predicted Fraction of Observed
= 0.5Prey Pool Found in X # of trials
ReproducibilityRate
of Prey
2
3
4
5
6
2 Rate
trials 0 Pool0.00 0.003 trials
0.00 0.00 0.00 0.00
H 0.00 T0.00H0.00H 0.00
H H 0.1 H 0.00H 0.00
0.2
0.01 0.00 0.00 0.00 0.00 0.00
T 0.00 H0.01H0.01T 0.01
H T 0.3 T 0.02H 0.00
0.4
0.07 0.01 0.02 0.03 0.04 0.05
H 0.19 T0.27T0.31H 0.34
T H 0.5 H 0.39T 0.10
0.6
0.01 0.00 0.01 0.01 0.01 0.01
T 0.03 H0.04T0.04T 0.04
T T 0.7 T 0.04T 0.02
0.8
0.17 0.11 0.15 0.16 0.17 0.17
0.9
0.00 0.00 0.00 0.00 0.00 0.00
Note:
1
0.31 0.31 0.31 0.31 0.31 0.31
•If hit = 3+ times
then
0.125
1.00
0.55probability
0.72 0.83 =
0.89
0.93
False Negative Rate
Predicted False Negative Rate
1
Fraction of Hit Pool
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
Reproducibility
Rate
0
0.1
0.2
0.3
# of trials
0.4
2
0.5
3
0.6
4
0.7
5
0.8
6
0.9
1
0.1
0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Reproducibility Rate
Lesson:
•1 or 2 trials provides highly
incomplete dataset.
Reproducibility
Rate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Theoretical Probability of NOT
Observing 2+ in X # of trials
2
3
4
5
6
1.00 1.00 1.00 1.00 1.00
0.99 0.97 0.95 0.92 0.89
0.96 0.90 0.82 0.74 0.66
0.91 0.78 0.65 0.53 0.42
0.84 0.65 0.48 0.34 0.23
0.75 0.50 0.31 0.19 0.11
0.64 0.35 0.18 0.09 0.04
0.51 0.22 0.08 0.03 0.01
0.36 0.10 0.03 0.01 0.00
0.19 0.03 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
Fraction
of Prey
Pool
0.00
0.00
0.01
0.02
0.07
0.39
0.01
0.04
0.17
0.00
0.31
1.00
Predicted Fraction of Prey
Pool NOT Found in X # of trials
2
3
4
5
6
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.01 0.01 0.01 0.01 0.01
0.05 0.04 0.03 0.02 0.01
0.29 0.19 0.12 0.07 0.04
0.01 0.00 0.00 0.00 0.00
0.02 0.01 0.00 0.00 0.00
0.06 0.02 0.01 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.45 0.28 0.17 0.11 0.07
Predicted False Positive Rate
Predicted False Postive Rate vs. Database
Frequency
1
False positive frequency
Method
-determine prey frequency in
database
-Assume background proteins have
a uniform random distribution
-Assume background does not
change with time or experimental
conditions
-Compare prey frequency to
predicted observation rate
< 0.05
0.9
0.8
0.7
0.6
0.5
# of trials
0.4
2
3
4
5
6
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
PathMap (global) observation frequency
risk ( p)  k  2
mn
n!
( p k )((1  p) n  k )
k!(n  k )!
E falsepositive (cutoff)   p 0
p  cutoff
risk ( p) Numhits( p)
p: prey observation frequency
“safe”
5%
n: number of trials
region
k: number of observations required (2)
Efalsepositive: expected number of false positives
cutoff: frequency cutoff
Numhits(p): number of hits at each prey observation frequency
1
Estimated Experimental False Positive Rate
Random Sampling Method
-randomly reassign bait labels for each IP for all 49 baits
-repeat
-obtain 3, 4, and 5 trial sets, 49 baits each, with preys randomly assigned to a bait
(5% database frequency)
-assume random distribution (no relation between baits)
Results
# trials
3
4
5
Observed reproduced hits
(false positives)
percent Calculated number
number
(n=190) of false postives
1
6
7
1%
3%
4%
<1
<2
<3
-false positive rate 2-3X greater than calculated.
-non-uniform distribution
Reasons
-not independent experiments
-non-random
-baits are related
-cross-contamination
-equipment contamination
Managing False Positives
1-Control subtraction
-empty vector immunoprecipitation
-irrelevant protein immunoprecipitation
2-Reproducibility
-2+ times
-3-4 biological replicates
3-Database frequency
-observation frequency cutoff
4- Prioritization
-annotation
5-Validation
-reciprocal immunoprecipitation
-co-expression
Interaction Network
Example
Human Pathway Pilot Project
TNFa pathway
-Proinflammatory cytokine expressed mainly by activated monocytes and macrophages
-Highly studied
-Pathway members provide ready availability of baits.
-Understanding incomplete, providing opportunity for discovery
-Disease involvement
-Tumor progression and killing
-Diabetes
-Infection
-Inflammation
-Pharmaceutical potential
-Find protein targets that perform isolated TNFa functions without side-effects.
Contract design:
-20 baits, chosen by customer (17 actually provided)
-N & C FLAG tags, constructed by MDSP.
-Report all observed interactions.
Additional design parameters:
-Expressed and immunoprecipitated 4 times each.
-Report all interactions classified as hits.
TNFα Pathway: Inflammation/Cancer
with Preys
- 17 Baits
- Both N & C tags
- 4 Immunoprecipitations
NK Cell
Function
TGFr
IL10RB
ENaC
CD40
IL10RA
Na
Channels
Fas
Endocytosis
Regulation
KIAAxxx
TNFr
CS1
xxx4
Rab5
SGK
xxx15
DNA repair/Damage
xxx14, xxx2
TRAF2
xxxF1
xxx1
xxxCA
TBK1
TANK
xxx1-L
Caspases
NIK
Stat
xxxB
Others
xxxL1, xxxC1, FLJxxx,
FLJxxx, xxx1, MGCxxx,
KIAAxxx,
FLJxxx, xxxA11
xxxA13
Xxxx
xxxA3
IKAP
Cell Death
IKK-1
Transcriptional Regulation
xxx8
FLJxxx
IKB
Src
Protein Transport
xxx13
NF-kB
Ptyr PP
xxx4, xxxA, xxxE,
xxxG1, xxxG2, xxx4
xxx5
B-xxx1
xxx12
TRAF3
Jak
xxx1A
xxx37
TRAF6
xxxCB
CLARP
RIPK2
FADD
Protein Sorting / Targeting
xxx23, xxx-SR, xxx3,
FLJxxx, xxx3, xxx4
xxxB12
xxx7
xxx19
KIAAxxx
xxx8
Gxxx
xxx-99
3-xxx
14-3-3 
xxx1
PPP
14-3-3 
xxx1D
xxx
xxx1
14-3-3 
xxx1
14-3-3 
xxx
FLJxxx
14-3-3 
xxx4
xxx
PP1CB
XRCC7
xxx11
GYS1
PPP1R3
xxxA1
Nucleus
Transcription
xxxL1
CDC2
xxx4
xxx
xxx14
xxxG4
xxxL1
kinase
A20
xxx130
SGK Gene
xxxA'
xxxA9
Transcription
xxxB
xxxA1
TNFa Bait Protein
Other TNFa Pathway protein
Prey protein
Interactions with Bait protein
???
xxxA8
Activation
Cyclin
xxxGP
KIAAxxx
PITSLRE(8)
???
Cell cycle
Control
Transcription
Inhibition
Causal (indirect) interactions
CS1/Jasmine/19A24 Gene
TNFa Pathway Project Summary
Bait information
baits
membrane baits
expressed
membrane baits expressed
baits with interactions
expressed baits with no TNFa context
Potential
antibody
targets
number comment
17
3
14 2 not expected to express
2
13
7
Bait/Prey information
preys
known interactions
new interactions
baits placed in context
new bait/prey/bait linkages
99
13
86
5
4 also observed 1 known linkage
Prey information
enzymes
37
proteins in druggable families
proteins with no function
hypothetical proteins
transmembrane (TM) domain containing proteins
7 TM
potential plasma membrane proteins
protease, GTPase, ATPase, kinase,
20+ phosphatase, receptor
13 6 enzymes, 1 receptor
4
15
1 receptor?
8 others ER or mitochondrial
Integrating Proteomic and
Genomic Information
Genes Regulating Cell Growth and Division
Systematic identification of pathways that
couple cell growth and division in yeast
Science 297: 395-400, 2002.
Paul Jorgensen
Joy L. Nishikawa
Bobby-Joe Breitkreutz
Mike Tyers
Program in Molecular Biology and Cancer
Samuel Lunenfeld Research Institute
Mount Sinai Hospital
Toronto, Ontario, Canada
Genetic Screen for Yeast Size Mutants
whi
lge
4812
strains
(~2 yrs)
Wild type
size profile
sfp1
whi
lge
10
35
60
85
Cell volume (fL)
110
SFP1 regulated genes
WT
GALSFP1
SFP1
GAL genes (10)
Nucleotide biosynthesis (12)
tRNA synthetases (6)
ribosome biogenesis (21)
RNA Polymerases I and III (10)
nucleolus (29)
Translation initiation
and elongation (17)
Ribosomal protein genes (136)
scale
5
3
1.5
-1.5
-3
-5
Yeast Interaction Map
Ho et al. Nature 10:180-3, 2002.
aFLAG IP > LC-MS/MS
-725 bait attempts
-493 baits > 1578 preys
-646 unannotated preys
Overlap of Genetic, Expression & Interaction
Data
Protein interactions
Genetic interaction
Common mRNA regulation
Nucleolar
Network
Gene Regulation in Breast Cancer
98 breast tumors x 25000 genes
“genes that are
overexpressed in
tumors with a poor
prognosis profile are
potential targets for the
rational development of
new cancer drugs”
430
van’t Veer et al. (2002) Nature 415, 530-6.
2460
Proteins in the
functional pathway of
disease associated
genes may identify
additional or better
231 therapeutic targets.
Overlap of PathMap and Breast Cancer Genes
MDSP
reporter
ER
BRCA1
Prognostic
Rosetta
2460
430
231
van’t Veer et al. (2002) Nature 415, 530-6.
primary
enz
194 8% 42
27 6%
7
28 12%
9
secondary
enz
515
87
208
38
27
4
Protein Networks in Prognosis Reporters
enzyme
+
55
only
35
up regulated
4
down regulated 16
Interaction network provides context
Integrated Genomic/Proteomic Breast
Cancer Project
reporter
# of genes
ER
BRCA1
Prognostic
2460
430
231
•Profile gene expression changes during tumor progression
•Assemble experimental gene set
-genes with expression changes
-genes suspect for breast cancer progression
•Perform IP-MS to determine interacting proteins
•Analyze for regulatory networks and critical pathways
van’t Veer et al. (2002) Nature 415, 530-6.