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

Properties of Interaction Networks

CS 374 PresentationSusan Tang

October 12, 2006

Protein Interactions

Protein interactions are ubiquitous and essential for cellular function

Signal transduction Metabolic pathway Transcription regulation

Protein Interaction: Cell Signaling

http://en.wikipedia.org/wiki/Phospholipase_C

Protein Interaction: Metabolic Pathway

http://www.phschool.com/science/biology_place/biocoach/images/transcription/eusplice.gif

Protein Interaction: Transcription Regulation

http://www.cifn.unam.mx/Computational_Genomics/old_research/FIG22.gif

Protein Interaction Network

Yeast Protein Interaction Network. Tucker et al. 2001.

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

Importance of Protein Interaction Networks

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)

http://media.biocompare.com/bcimages/techart/cytofig1.jpg

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. 2004

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. 2000.

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. 2004

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. 2005.

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.

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 )

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

In-Paralogs Out-Paralogs

C

http://genomebiology.com/content/figures/gb-2001-2-4-comment1005-1.jpg

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

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

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

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

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

Interolog Mapping: Schema

H. pylori (bacteria) C. elegans (worm)

D. melanogaster(fly) S. Cerevisiae (yeast)

S. cerevisiae(yeast)

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

Interolog Mapping: Sequence Similarity and Interaction Transfer

Weighted Average of all 4 mappings

70

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%

Interolog Mapping: Trade-Offs

Increase JE Increase Accuracy

Decrease Predictive Power

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

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

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.

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

Party Hub: Example (Supreme Court)

Stephen G Breyer

Samuel Alito, Jr.

Ruth Bader GinsburgJohn Roberts (Chief of Justice)

David H. Souter

Clarence Thomas

Anthony Kennedy

Antonin Scalia John Paul Stevens

Date Hub: Example (Presidential Cabinet)

Margaret Spellings

Elaine ChaoDept of Labor

Condoleeza Rice(Secretary of State)

George Bush

Samuel Bodman (Dept of Energy)

Alberto GonzalesDepartment of Justice

Michael O. Leavitt(Dept of HHS)

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

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

Interaction Network Modularity: PCC Distribution

Interaction Network ModularityPrediction 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 45 data points Sporulation 10 data points Unfolded protein response 9 data points Stress response 174 data points Cell cycle 77 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

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

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)

Removal (Party Hub)

Removal (Date Hub)

FYI Network

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

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

Interaction Network Modularity: Date/Hub Representation of FYI

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