1. Protein Interaction Networks
Thanks to Mehmet Koyuturk

2. Protein-Protein Interactions
7. Protein Interaction Networks
Protein-Protein Interactions
Physical association between proteins
Signal transduction, phosphorylation
Docking, complex formation
Permanent vs. transient interactions
Co-location of proteins
Proteins that work in the same cellular component
Soluble location: lysosome, mitochondrial stroma
Membrane location: receptors in plasma membrane, transporters in mitochondrial membrane
Functional association of proteins
Proteins involved in the same biomolecular activity
Enzymes in the same pathway, co-regulated proteins

3. Permanent vs Transient Interactions
7. Protein Interaction Networks
Permanent vs Transient Interactions
Permanent interactions
Some proteins form a stable protein complex that carries out a structural or functional biomolecular role
These proteins are protein subunits of the complex and they work together
ATPase subunits, subunits of nuclear pore
Transient interactions
Proteins that come together in certain cellular states to undertake a biomolecular function
DNA replicative complex, signal transduction

4. Signal Transduction Phosphorylation Signaling cascade
7. Protein Interaction Networks
Signal Transduction
Phosphorylation
Protein-kinase interaction
Enzyme activation
Signaling cascade

5. Why Study Protein Interactions?
7. Protein Interaction Networks
Why Study Protein Interactions?
Identification of functional modules and interconnections between these modules
Functional annotation based on binding partners and interaction patterns
Identification of evolutionarily conserved pathways
Identification of drug target proteins to minimize side effects

6. Identification of Protein Interactions
7. Protein Interaction Networks
Identification of Protein Interactions
Traditionally, protein interactions are identified by wetlab experiments based on hypotheses on candidate proteins
Small scale assays
Coimmunoprecipitation: Immunoprecipitate one protein, see if other is also precipitated
Reliable, but can only verify interactions between suspected partners
High throughput screening
Throw in thousands of ORFs and see which ones bind to each other
Yeast two hybrid, tandem affinity purification
Large scale, but a lot of noise

7. 7. Protein Interaction Networks
Yeast Two Hybrid
Split yeast GAL4 gene, which encodes a transcription factor, required for activation of GAL genes in two parts
Activating domain, binding domain
The split protein does not work unless the two parts are in physical contact

8. Protein Interaction Networks
Organize all identified interactions in a network, where proteins are represented by nodes and interactions are represented by edges
TAP identifies a group of proteins that are caught by target protein
Spoke model (star network) vs. matrix model (clique)
Interaction
Protein

9. Functional Modularity in PPI Networks
7. Protein Interaction Networks
Functional Modularity in PPI Networks
A protein complex
Dense subgraph
A signal transduction pathway
Simple path, parallel paths
A protein with common, key,
fundamental role (e.g., a kinase)
Hub node

10. Computational Prediction of PPIs
7. Protein Interaction Networks
Computational Prediction of PPIs
Functional association is a higher level conceptualization of interaction
Proteins that act as enzymes catalyzing reactions in the same metabolic pathway
Functionally associated proteins are likely to show up in similar contexts
Co-regulation, co-expression, co-evolution, co-citation…
Functional association between proteins can be computationally identified by looking at different sources of data such as sequences, gene expression, literature
Can also be extended to capture physical associations, for example, by taking into account evolution at structural level

11. Conservation of Gene Neighborhood
7. Protein Interaction Networks
Conservation of Gene Neighborhood
In bacteria, the genome of an organism is organized in such a way that that functionally related proteins are coded by neighboring regions
Operons
When more than one bacterial species are considered, it is observed that this neighborhood relationship becomes even more relevant
Distribution of neighboring genes in
H. Influenzae and E. coli into functional
classes

12. Comparison of Nine Bacterial Genomes
7. Protein Interaction Networks
Comparison of Nine Bacterial Genomes
trpB-trpA is the only gene pair whose proximity is conserved across nine prokaryotic genomes
These genes encode the two subunits of tryptophan synthase that interact and catalyze a single reaction

13. Close Orthologs Run of genes Bidirectional best hits
7. Protein Interaction Networks
Close Orthologs
Run of genes
A set of genes on one strand, such that gaps between adjacent genes is less than a threshold, g (in practice, g  300 bp)
Any pair of genes on the same run are said to be close
Bidirectional best hits
Genes X1 and X2 from genomes G1 and G2 are BBH, if their sequence similarity is significant and there are no Y1 (Y2) in G1(G2) that is more similar to X2 (X1) than X1 (X2)
Pair of close bidirectional best hits: Xa, Ya close in G1, Xb, Yb close in G2, Xa&Xb BBH, Ya& Yb BBH

14. Predicting Interactions
7. Protein Interaction Networks
Predicting Interactions
For each pair of close orthologs (occuring at least one pair of genomes), calculate a score
Score should increase with the phylogenetic distance between the two genomes, since closely related organisms are more likely to have similar genes nearby due to chance alone
Existence of a triplet (P1, P2, P3) should be stronger than the existence of two pairs (P1, P2 and P1, P3)
Triplet distance can be estimated as the minimum distance between any pair of organisms (in addition to pair score)

15. Reconstructing Pathways
7. Protein Interaction Networks
Reconstructing Pathways
Purine Metabolism
Can identify the association between unknown proteins and known pathways!

16. Projection of Gene Neighborhood
7. Protein Interaction Networks
Projection of Gene Neighborhood
The composition of operons is evolutionarily variable
A particular set of functionally related genes do not always comprise an operon
The application of gene neighborhood based interaction prediction is limited for a single organism
With multiple organisms, it is possible to statistically strengthen conclusions and project findings on other organisms
If an operon with functionally related genes exists in several genomes, a functional association can be predicted for other organisms, even if the corresponding genes are scattered
Variability turns out to be an advantage for prediction

17. Gene Neighborhood - Limitations
7. Protein Interaction Networks
Gene Neighborhood - Limitations
It is only directly applicable to bacteria (and archaea), because relevance of gene order does not necessarily extend to eukaryotes
For closely related species, conserved gene order might just be due to lack of time for genome rearrangements
We are interested in selective constraints that preserve gene order
Compared species should be distant enough
But not too distant, because we need sufficient number of orthologs to be able to derive statistically meaningful results

18. Gene Fusion Domain fusion events
7. Protein Interaction Networks
Gene Fusion
Domain fusion events
Two protein domains that act as independent proteins (components) in one organism may form (part of) a single polypoptide chain (composites) in another organism
Most proteins that are involved in domain fusion events are known to be subunits of multiprotein complexes (76% in E. coli metabolic network)

19. Gene Fusion Based PPI Prediction
7. Protein Interaction Networks
Gene Fusion Based PPI Prediction
A pair of proteins in query genome are candidate interacting pairs if
They show (local) sequence similarity to the same protein (rosetta stone) in reference genome
They do now show sequence similarity with each other
Complete genomes!

20. Predicted Interactions
7. Protein Interaction Networks
Predicted Interactions
Known physical
interactions
same pathway
Proteins in the

21. Gene Fusion Based Prediction - Results
7. Protein Interaction Networks
Gene Fusion Based Prediction - Results
Interactions predicted based on gene fusion events
Distance on circle shows distance on genome

22. Co-evolution of Interacting Proteins
7. Protein Interaction Networks
Co-evolution of Interacting Proteins
Selective pressure is likely to act on common function
Proteins that are interacting are expected to either be conserved together along with their interactions, or not conserved at all
Hypothesis 1: Orthologs of interacting proteins also interact in other species (supported by evidence, but there are subtleties, which we will discuss this later)
Hypothesis II: If two proteins are
interacting, then they will show
similar conservation patterns
Phylogenetic profiles

23. Phylogenetic Profiles
7. Protein Interaction Networks
Phylogenetic Profiles

24. Correlation of Phylogenetic Profiles
7. Protein Interaction Networks
Correlation of Phylogenetic Profiles
Assume we have N genomes, protein X has homologs in x of them, Y has y, and they co-occur in z genomes
Hamming distance:
Pearson correlation:
Mutual information:
Statistical significance:

25. Phylogenetic Profiles - Limitations
7. Protein Interaction Networks
Phylogenetic Profiles - Limitations
Many processes may be common across lineages
Too many false positives
Database of genomes may be biased
All organisms are treated equally
Improvement: Use trees instead of profiles
Proteins are assumed to be conserved as a whole
It is domains that interact
Improvement: Use domain profiles
Yeast nucleoli and ribosomal proteins
Organisms

26. Phylogenetic Tree Based Prediction
7. Protein Interaction Networks
Phylogenetic Tree Based Prediction
Phylogenetic trees of Ntr-family two-component sensor histidine kinases and their corresponding regulators

27. 7. Protein Interaction Networks
Mirror Tree Method
Need to have sufficient number of genomes that contain homologs of both proteins

28. 7. Protein Interaction Networks
Matrix Method
Start with families of proteins that are suspected to interact
Identify specific pairs of proteins that interact by aligning the phylogenetic trees that underly the two families
Assumption: Identical number of proteins in each family

29. 7. Protein Interaction Networks
Correlated Mutations
Co-evolution of interacting proteins can be followed more closely by quantifying the degree of co-variation between pairs of residues from these proteins
Correlated mutations may correspond to compensatory mutations that stabilize the mutations in one protein with changes in the other
Distribution of distances between aminoacid positions on a folded protein

30. 7. Protein Interaction Networks
In silico Two-Hybrid
The correlation of mutations between two positions (may be on different proteins) can be estimated from pairwise assessment of aligned multiple sequences
Position pairs with high correlation are potential contact points
Interaction index
For a protein pair, compute the aggregate correlation (of mutations) across all positions

31. 7. Protein Interaction Networks
In silico Two-Hybrid

32. 7. Protein Interaction Networks
Performance of I2H
I2H predicts physical, rather than functional association
It requires complete genomes & sufficient number of homologs

33. Co-citation Based PPI Prediction
7. Protein Interaction Networks
Co-citation Based PPI Prediction
Functionally associated proteins are likely to be cited in the same research article
We can assess the statistical significance of co-citation based on hypergeometric model
Algorithmic problem: How to recognize & match protein names?
Train algorithm using annotated abstracts via conditional random fields (CRF)

34. Performance of Co-citation
Statistical significance is quite relevant until it saturates
The method is robust to choice of parameters for name recognition

35. Integrating PPI Networks
7. Protein Interaction Networks
Integrating PPI Networks
Interaction data coming from multiple sources
Different sources refer to different levels of interaction
Can integration handle noise, making interaction data more reliable?
Superpose interactions based on their reliability

36. 7. Protein Interaction Networks
Bayesian Integration
For each prediction method, compute log-likelihood score
Let P(L|E) be the number of interactions predicted by method E, such that functional association between corresponding proteins is known
Let ~P(L|E) be the number of false positives
Let P(L) and ~P(L) be the corresponding priors
Assign weights to methods based on their log-likelihood scores

37. Comparison of Prediction Methods
7. Protein Interaction Networks
Comparison of Prediction Methods
Integrated network captures functional association better
Note that the integrated network is “trained” using available data on functional association

38. Classification Based Integration
7. Protein Interaction Networks
Classification Based Integration
Points: Proteins, Space: Expression, Conservation, Labels: Function
Points: Protein Pairs, Space: Co-expression, Co-evolution, etc., Labels: Existence of Interaction

39. Performance of Domain Co-evolution
7. Protein Interaction Networks
Performance of Domain Co-evolution

40. Co-Evolutionary Matrix
7. Protein Interaction Networks
Co-Evolutionary Matrix

41. Domain Identification
7. Protein Interaction Networks
Domain Identification

42. Difference between Predicted PPIs
7. Protein Interaction Networks
Difference between Predicted PPIs