Download - Protein-Protein Interactions Networks
Protein-Protein Interactions Networks
“A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae”P.Utez et al, Nature 2000
“Functional organisation of the yeast proteome by systematic analysis of protein complexes” G. Gavin et al, Nature 2002 “Global Mapping of the Yeast Genetic Interaction Network” Tong et al, Science 2004
“Global analysis of protein activities using proteome chips” Zhu, H. et al. Science 2001
“Conserved patterns of protein interaction in multiple species” R. Sharan et al, PNAS 2005
Genomics
Genomics – “The
large scale study
of genomes and
their functions”
Why protein
network?
Why protein network?
Assemblies represent more than the sum of their parts.
`complexity' may partly rely on the contextual combination of the gene products.
24,000 genes24,000 genes 26,000 genes26,000 genes 50,000 genes50,000 genes
19,50019,500 genesgenes14,00014,000 genesgenes
Yeast as a model
Why yeast genomics?
A model eukaryote organism …
Saccharomyces cerevisiaeSaccharomyces cerevisiae
The best-studied organism
~5,500 genes.
16(!) chromosomes.
13 Mb of DNA (humans have ~3,000 Mb).
We know (?) the function of >1/2 of the
yeast genes.
All the essential functions are conserved
from yeast to humans.
Example: cell cycle
Lee Hartwell, Nobel Prize 2001
4 methodologies for high throughput research
Two hybrid systems
Analysis of protein complexes
Synthetic lethal
Protein Chips (?)
Two hybrid system
Aim: Identify pairs of Physical interactions.
Solution: Use the transcription mechanism of the
cell
3
TRANSCRIPTION
DNA
RNA
TRANSLATION
PROTEIN
The central dogma
Transcription factors
Movie – transcription (molecular model, real time) 7.2
Transcription – real time( viedo)
Eukaryotic mRNA
Reporter gene
Two hybrid system
Isolate double plasmids using reporter or selection methods.
All against All
Focus on the baits
Baits are analyzed separately. 192 baits vs. ~6000 pray yeast
strains.
A component of RNA polymerase I, III, identification of three new interacting proteins
Two hybrid system
Two hybrid system“A comprehensive two-hybrid analysis to explore the yeast
protein interactome“ Ito T. et al, PNAS 2001.
Analysis of protein complexes
Aim: Identification of complexes and their sub units.
Solution: a two step method Isolation of only relevant complexes Identification of complex units.
Double Isolation
Identification of the members
Divide and conquer-
• Digest with protease
• Mass spectrometry
• Denaturate assembly
How does it work?
The deflection route of ionized molecules is used to determine the molecule’s mass.
The output:
Analysis of protein complexes Cross results of peptide mass with protein database.
Mass spectrometry can be implied again if the data is not sufficient, this time for the peptides.
Analysis of protein complexes• Systematic(1): 1739 bait proteins.
• 232 complexes with 589 baits.• Systematic(2): 725 bait proteins.
• 3,617 interactions with 493 baits.
Analysis of protein complexes
About 25% false positive rate. Covers 56/60%, 10/35% in Y2H, of known
complexes. Only 7% of the interactions were seen by Y2H
assays.
But, Can evaluate protein-
Concentration. Localization. Post-translational modifications.
Synthetic lethality
First, few words on essentiality. Create new strains, each strain with one
gene deleted (96% coverage) Tag each strains with a unique sequence. Grow all the strains. Measure the amount of each seq. Some 18.7% (1,105) are essential.
Synthetic lethality
High genetic redundancy hardens the discovery of many gene functions (30%).
Only the double mutation is lethal, either of the single mutations is viable.
Why? Single biochemical pathway. Two distinct pathways for one process. …
The naïve approach
But how do you genomics it? …
All vs. All
~5100 non essential mutants.
• Main tricks: 1. Haploid strains
2. Resistant markers.
3. Extra marker for the library haploid.
Synthetic lethality …Making it genomics
Mass analysis: Crossing the query haploid with a library (synthetic genetic array)
Tetrad analysis: Validation and finding synthetic sick
The genetic interaction map 8 genes against all produced a network of synthetic
lethal pairs.
Synthetic lethality …Making it genomics
132 query genes vs. 4700 False negatives – 17-42%. At least 4 times more dense than the PPI network. Predicting ~100,000 interactions (?)
PPI Summery (2003)
PPI Summery
S. Cerevisiae (Yeast)• 4389 proteins• 14319 interactions
C. Elegans (Worm)• 2718 proteins• 3926 interactions
D. Melanogaster (Fly)• 7038 proteins• 20720 interactions
Sharan et al. PNAS 2005
We like Networks
Exploit graph theory methods.
Provide a general solution for data integration.
Network Structure and Function
Identify highly nonrandom network structural patterns that reflect function: Ideker et al: Finding co-regulated sub-graphs.
Lee at el: The repeated instances of each motif are the result of evolutionary convergence.
Barabasi at el: Network motifs are associated with specific cellular tasks.
…
Baker’s yeast(Saccharomyes
cerevisiae)~15000 interactions~5000 interacting
genes
Bacterial pathogen
(Helicobacter pylori)~1500
interactions~700 interacting
genes
Conserved patterns of PPI in multiple species
Kelley et al. PNAS 2003
Goals
Separating true PPI from false positives. Assign functional roles to interactions. Predict interactions. Organizing the data into models of cellular
signaling and regulatory machinery.
How? Use approach based on evolutionary cross-species
comparisons.
Interaction graph (per species)
Vertices are the organism’s interacting proteins. Edges are pair-wise interactions between proteins. Edges are weighted using a logistic regression model:
A: Number of times an interaction was observed. For Fly and worm observation In one experiment.
B: Correlation coefficient of the gene expression. Shown to be correlated to interaction.
C: Proteins’ small world clustering coefficient. Sum of the neighbors logHG probs.
How do we find Sub-network conservation?
Interactions within each species should approximate the desired structure: Pathway. Signal transduction. Cluster. Protein complex.
Many-to-many correspondence between the sets of proteins.
Network alignment graph Each node corresponds to k sequence-similar proteins.
BLAST E value < -7; considering the 10 best matches only. Cannot be split into two parts with no sequence similarity between
them.
Edge represents a conserved interaction. Match -> One pair of proteins directly interacts and all other
include proteins with distance <2 in the interaction maps. Gap –> All protein pairs are of distance 2 in the interaction maps. Match-Gap-> At least max{2, k −1} protein pairs directly interact.
A subgraph corresponds to a conserved sub-network.
q(e) – interaction similarity
A probabilistic model
PS Pe randomq
eqlog
Searching for conserved sub-networks
Identifying high-scoring subgraphs of the network alignment graph. …This problem is computationally hard.
Exhaustively we find seeds - paths with 4 nodes. Expand high scoring seeds. Greedily add/remove
nodes. Filter subgraphs with a high degree of overlap
(>80%).
Statistical evaluation of sub-networks
Randomized data is produced: Random shuffling of each of the interaction graphs. Randomizing the sequence-similarity relationships.
Find the highest-scoring sub-networks of a given size.
P-value is computed by the distribution of the top scores.
← P
rote
in s
eq
uen
ce
sim
ilari
ty →
← Bacteria →
← Yeast →
The final product
3-way Comparison
S. cerevisiae• 4389 proteins• 14319 interactions
C. elegans• 2718 proteins• 3926 interactions
D. melanogaster• 7038 proteins• 20720 interactions
Sharan et al. PNAS 2005
Multiple Network Alignment
Preprocessing
Interaction scores: logistic regression on #observations, expression correlation, clustering coeff.
Network alignmentSubnetwork search
Filtering & Visualizing
p-value<0.01, 80% overlap
Conserved paths
Conserved clustersProtein groups
Conserved interactions
Reduced false positives
Compared these conserved clusters to known complexes in yeast - Pure cluster - contain >2 annotated proteins and
>1/2 of these shared the same annotation. 94%(>83% in mono specie) pure clusters.
Did ‘‘sticky’’ proteins biased the clusters? Of 39 proteins (> 50 neighbors), only 10 were
included in conserved clusters. And they were annotated so.
Cross Validation: Function
Species #Correct #Predictions Success rate (%)
Yeast 114 198 58
Worm 57 95 60
Fly 115 184 63
Outperforms sequence-based approach at 37-53%.
Guilty by association. Enrichment of GO annotation (p<0.01). More then half of the annotated proteins had the annotation.
Cross Validation: Interaction
Species Sensitivity (%)
Specificity (%)
P-value Strategy
Yeast 50 77 1e-25 [1]
Worm 43 82 1e-13 [1]
Fly 23 84 5e-5 [1]
Yeast 9 99 1e-6 [1]+[2]
Worm 10 100 6e-4 [1]+[2]
Fly 0.4 100 0.5 [1]+[2]
[1] Evidence that proteins with similar sequences interact within other species.
[2] Co-occurrence of these proteins in the same conserved cluster.
Wet Validation: Interaction
The tests were performed by using two-hybrid assays. Of the 65 yeast predicted interactions:
5 were self inducing. 31 tested positive.
Conclusions
Associate proteins that are not necessarily each other’s best sequence match. 177/679 conserved clusters. 31/129 conserved paths.
Inter module interaction is reinforced by inter-species observations.
40-52% >> 0.042% as a random PPI prediction.
Many PPI circuits are conserved over evolution.
Thanks!!!
Recoverin, a calcium-activated myristoyl switch.
GO – Gene Ontology
all : all ( 171472 ) GO:0008150 : biological_process ( 109503 )
GO:0007582 : physiological process ( 70981 ) GO:0008152 : metabolism ( 41395 )
GO:0009058 : biosynthesis ( 10256 ) GO:0009059 : macromolecule biosynthesis (
6876 ) GO:0006412 : protein biosynthesis
( 4611 ) GO:0043170 : macromolecule metabolism ( 17198 )
GO:0009059 : macromolecule biosynthesis ( 6876 )
GO:0006412 : protein biosynthesis ( 4611 )
GO:0019538 : protein metabolism ( 12856 ) GO:0006412 : protein biosynthesis
( 4611 ) GO:0005575 : cellular_component ( 98453 ) GO:0003674 : molecular_function ( 108120 )
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Interaction distribution
Expression data
Yeast - 794 conditions. Fly - over 90 CC time points+170 profiles. Worm - over 553 conditions.
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Edge weight
where 0, . . . , 3 are the parameters of the distribution.
Maximize the likelihood: Positive: MIPS interactions. Negative: random or false positives in the cross
validation test. Yeast - 1006 positive and negative examples. Fly - 96 positive and negative examples. Worm – 24 positive and 50 negative examples.
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71 conserved regions: 183 significant clusters and 240 significant paths.back
A probabilistic model Ms - the sub-network model. Mn - the null model. Ouv - the set of available observations on u-v. Puv- fraction of (u,v) in order preserving graphs family. T/Fuv – True/False edge (u,v).
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A probabilistic model
Each species’ interaction map was randomly constructed.
Randomizing assumptions: Each interaction should be present independently
with high probability. The probability depends on their total number of
connections in the network.
Why Yeast?
“Comparative Genomics of the Eukaryotes” Rubin GM. et al. Science 2000 back
Analysis of protein complexes1. Isolation:
A straight forward method, using Affinity chromatography. A target protein is attached to polymer beads that are packed into a column. Cell proteins are washed through the column.Proteins the interact with the target protein adhere to the affinity matrix and are eluted later.
Analysis of protein complexes2. Isolation:
Co-immunoprecipitation. An antibody that recognizes the target protein is used to isolate the protein. Usually the there isn’t a highly specific antibody for the target protein. A chimera protein is formed, using a the target protein and an epitope tag.The common tag is a enzyme glutathione S-transferase (GST).
Analysis of protein complexes2. Isolation:
Isolation of complex using the Chimera
Glutathione coated beads
Cell extract
Glutathione solution
MIPS
Munich Information Center for Protein Sequences (MIPS).
Hierarchy Structure. Only manually annotated complexes from DIP. Left with 486 proteins spanning 57 categories at level
3.
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