1. 10/12: “Properties of Interaction Networks”
Presenter: Susan Tang
Scriber: Neda Nategh
DFLW: Chuan Sheng Foo
Upcoming:
10/17: “Transforming Cells into Automata” Ravi Tiruvury
“Index-based search of single sequences” Omkar Mate
10/19: “Multiple indexes and multiple alignments” Siddharth Jonathan
“Human Migrations” Anjalee Sujanani

2. Properties of Interaction Networks
CS 374 Presentation
Susan Tang
October 12, 2006

3. Protein Interactions
Protein interactions are ubiquitous and essential for cellular function
Signal transduction
Metabolic pathway
Transcription regulation

4. Protein Interaction: Cell Signaling

5. Protein Interaction: Metabolic Pathway

6. Protein Interaction: Transcription Regulation

7. Protein Interaction Network
Yeast Protein Interaction Network. Tucker et al

8. Importance of Protein Interaction Networks
Studying protein interaction network architecture allows us to:
Assess the role of individual proteins in the overall pathway
Evaluate redundancy of network components
Identify candidate genes involved in genetic diseases
Sets up the framework for mathematical models
For complex systems, the actual output may not be predictable by looking at only individual components:
The whole is greater than the sum of its parts

9. Protein Interaction Data
High-throughput experiments
Yeast 2 Hybrid Screens
Co-IP
Experimental flaws
False positives / False negatives
Self-activators
Promiscuous proteins
Protein concentration differences
Lack of benchmark
Yeast 2 Hybrid Screen
(Cytotrap System)

10. Protein Interaction Data
Figure 1. Network cross-comparison. Pairs of proteins have been binned according to their shortest path in networks generated from Y2H and Co-IP data. The false-color map indicates bins with more (red) or fewer (blue) interactions than expected by chance. Bins enriched for true positives, false positives and true noninteractors are indicated.
Gaining confidence in high-throughput protein interaction networks. Bader et al

11. Protein Interaction Data
Validation
mRNA co-expression
genetic interactions
database annotations / keywords
Analysis based on validation studies show that only 30 – 50 % of high-throughput interactions are valid.
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. Gasch et al

12. Protein Interaction Data: Verification
Figure 4. Joint analysis of physical and genetic interactions. Genetic interactions have been used as anchors to mine the physical interaction network. Lines indicate high-confidence physical interactions (blue), genetic interactions (red) and physical + genetic interactions (black). Protein color indicates biological process (red, cell cycle; green, cell defense; cell environment, yellow; cell fate, yellow; cell organization, magenta; metabolism, lavender; protein fate, blue; protein synthesis, cyan; transcription, brown; transport mechanisms, tan; gray, no annotation).
Gaining confidence in high-throughput protein interaction networks. Bader et al

13. Interaction Networks: Features
Network Conservation Across Species
Comparison between yeast, fly, worm
3 eukaryotic species with most complete networks
71 network regions are conserved across all 3 species
Conserved patterns of protein interaction
in multiple species. Sharan et al

14. Interaction Networks: Literature
Interactions can be mapped from one genome to another through comparative genomics
Annotation Transfer Between Genomes: Protein-Protein Interologs and Protein-DNA Regulogs
Yu et al.
By correlating gene expression profiles for a hub and its partners, we can predict whether it’s a date or party hub
Evidence for dynamically organized modularity in the yeast protein-protein interaction network
Han et al.

15. Interolog Mapping: Background
Homology-based function annotation
Sequence similarity  structural similarity  functional similarity
Protein function is a vague term and difficult to compare
Focus on one aspect of protein function: Interactions with other proteins
Examine the accuracy of comparing sequences to extrapolate protein interactions
Functional similarity = f (Sequence similarity)
Protein interactions = f (Joint sequence similarity of interaction pair )

16. Interolog Mapping: Protein Homology
Homologs = proteins with significant sequence similarity (E-value<=10-10 )
Homologs encompass orthologs and paralogs
Paralogs = proteins in the same species that arose from gene duplication
DIFFERENT FUNCTION
Orthologs = proteins in different species that evolved from a common ancestor by speciation
SAME FUNCTION
Out-Paralogs
In-Paralogs C

17. Interolog Mapping: Orthologs
Interest in Orthologs
Key concept: If A and B interact in one species  orthologs A’ and B’ will interact
(A’ & B’) = “interologs” of (A & B)
Defining Orthologs
Loose definition: Top-blast hit
Stringent definition: Reciprocal top-blast hit
Not all orthologs can be found using above definitions
Maintain function  Maintain interactions

18. Interolog Mapping: Interaction Transfer
Previous Works
Best-match mapping
Reciprocal best-match mapping
Disadvantages:
Low coverage of total set of interactions
Low prediction accuracy
Limitations of Interaction Transfer
Some networks are more complete than others
Proportion of proteins that is annotated
Proportion of protein interaction partners recorded

19. Interolog Mapping: New Method
Generalized Interolog Mapping
Search for all homologs of each interacting protein  homolog family
Generalized interologs = any protein from family 1 + any protein from family 2

20. Interolog Mapping: Sequence Similarity Measures
Joint Sequence Similarity
Many ways to define joint sequence similarity
2 definitions are used here
Joint Sequence Identity
Joint E-Value
JE less biased in shorter sequences than JI
Prediction Accuracy vs. JE and Prediction Accuracy vs. JI plots convey similar trend

21. Interolog Mapping: Data
Gold Standard Positives P
Known interacting protein pairs in target organism
Loose definition of an interaction: does not have to be a physical interaction; can be via a complex association
8250 unique interactions in yeast
Gold Standard Negatives N
Known non-interacting protein pairs in target organism
Extracted/estimated from knowledge about protein localization
2,708,746 non-interactions in yeast

22. Interolog Mapping: Schema
S. cerevisiae(yeast)
H. pylori (bacteria)
S. Cerevisiae (yeast)
C. elegans (worm)
D. melanogaster(fly)

23. Interolog Mapping: Quantitative Parameters
Verification
V(J) = percentage of verified predictions among generalized interologs using J
Likelihood Ratio
L(J) = likelihood that a generalized interolog is a true prediction
Opost = L(J) Oprior
Naïve Bayesian network  no correlations between features  iterative use of different L’s
Opost/Oprior

24. Interolog Mapping: Sequence Similarity and Interaction Transfer
Weighted Average of all 4 mappings 70

25. Interolog Mapping: Comparison to Other Methods
By the numbers…
Applies to C.elegans  S.cerevisiae mapping only
Best-Match
Reciprocal Best-Match
Generalized Interolog (all)
Generalized Interolog (top 5% JE )
Predicted 84 33
9317
112
Validated 25 18
162 35
Accuracy
30%
54% 2%
31%

26. Interolog Mapping: Trade-Offs
Increase JE  Increase Accuracy
Decrease Predictive Power

27. Interolog Mapping: Experimental Verification
PIE (Probabilities Interactome Experimental) = 4 large-scale yeast interaction data sets
ROC curves compare
generalized interolog mapping
PIE
Generalized interlog mapping: coverage and accuracy comparable to PIE

28. Interolog Mapping: Summary
Finding
Higher joint sequence similarity  Higher accuracy of protein interaction transfer
Application
Can use interolog mapping method developed in paper to predict interactions in model organisms with less-complete interaction networks

29. Interaction Networks: Literature
Interactions can be mapped from one genome to another through comparative genomics
Annotation Transfer Between Genomes: Protein-Protein Interologs and Protein-DNA Regulogs
Yu et al.
By correlating gene expression profiles for a hub and its partners, we can predict whether it’s a date or party hub
Evidence for dynamically organized modularity in the yeast protein-protein interaction network
Han et al.

30. Interaction Network Modularity: Background
Interaction networks are scale-free
Most proteins interact with a small number of partners
A few proteins (“hubs”) interact with many partners
Resistant to random node removal
Sensitive to targeted hub removal
Types of Hubs
Party Hubs
Interact with most of their partners simultaneously
Perform specific functions inside module
Date Hubs
Interact with different partners at different times or locations
Connect modules (biological processes) together

31. Party Hub: Example (Supreme Court)
David H. Souter
Samuel Alito, Jr.
Stephen G Breyer
John Roberts (Chief of Justice)
Clarence Thomas
Ruth Bader Ginsburg
Anthony Kennedy
Antonin Scalia
John Paul Stevens

32. Date Hub: Example (Presidential Cabinet)
Margaret Spellings
Elaine Chao
Dept of Labor
Condoleeza Rice
(Secretary of State)
George Bush
Michael O. Leavitt
(Dept of HHS)
Alberto Gonzales
Department of Justice
Samuel Bodman
(Dept of Energy)

33. Interaction Network Modularity: Network Construction
Filtered Yeast Interactome(FYI)
Input Methods
High-throughput yeast-2-hybrid projects
Co-IP
Computational predictions
MIPS protein complexes
MIPS physical interactions
Procedure
Extract high-confidence interactions in yeast
High confidence = observed by atleast 2 different input methods
Results
1,379 proteins in this set
Average: 3.6 interactions per protein
Largest component: 778 proteins connected

34. Interaction Network Modularity: Hub Identification
Any node (protein) with more than 5 edges ( k > 5 )

35. Interaction Network Modularity: Hub Characterization
Data Source
mRNA gene expression profiles
Data for 5 different conditions
Pearson Correlation Coefficients (PCC)
Hub vs. Non-Hub
Calculate PCC for a hub and each of its partners  take average
Calculate PCC for a non-hub and each of its partners  take average
Look at distribution of average PCC
Hubs have a bi-modal distribution
Non-hubs have a normal distribution centered near 0

36. Interaction Network Modularity: PCC Distribution

37. Interaction Network Modularity Prediction of Date vs. Party Hub
Yeast Expression Compendium
Superset of data for all external conditions
Bi- modal: suggests we can partition date hubs from party hubs
Yeast Expression Conditions
Pheromone treatment data points
Sporulation data points
Unfolded protein response 9 data points
Stress response data points
Cell cycle data points
Date/Party Partition
Party Hubs = nodes with average PCC > cutoff in >= 1 conditions
Absence of clear bi-modal
Presence of clear bi-modal

38. Interaction Network Modularity: In Silico Node Removal
Effect on Path Connectivity
Characteristic path length = average shortest path length between node pairs
Remove node  observe change in characteristic path length
Is there a difference in path connectivity change for removal of party vs. date hubs? YES
Party hubs: connectivity not affected
Date hubs: connectivity decreased

39. Interaction Network Modularity: In Silico Node Removal
Effect on Remaining Components
Is there a difference in main component after node removal for party vs. date hubs? YES
Main Component (Remove party hub) >> Main Component (Remove date hub)
FYI
Network
Removal (Date Hub)
Removal (Party Hub)

40. Interaction Network Modularity: In Silico Node Removal
Date Hub Subnetworks
Each subnetwork has a tendency to be homogeneous in function
Subnetworks  biological modules
Can assign a ‘most likely’ function for each subnetwork by examining functional annotation

41. Interaction Network Modularity: Genetic Interactions
Organized modularity model predicts that genetic perturbations of party hubs should differ from those of date hubs
Genetic Perturbation
Date hubs and party hubs are comparable in terms of functional essentiality
Date hubs have more genetic interactions than party hubs

42. Interaction Network Modularity: Date/Hub Representation of FYI

43. Interaction Network Modularity: Summary
Findings
In silico investigation and genetic interaction analysis both describe a protein interaction model where:
there is organized modularity
date hubs act as module connectors
party hubs function at a lower level within modules.
Application
Use this prediction method to classify and organize other interactomes into a modular network
Identification of party and date hubs may provide insight into potential drug targets