prot2004 4514

Protein Evolution, Co-evolution and Interaction Networks(Day 3): 

Protein Evolution, Co-evolution and Interaction Networks (Day 3) Matteo Pellegrini Rosetta Inpharmatics

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Protein Networks Matteo Pellegrini Protein Pathways Inc.

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Outline Introduction Methods to infer protein couplings Direct detection of protein-protein interactions Analysis of expression data Properties of protein networks Applications of protein networks Identifying genes involved in osteoclast differentiation

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Escherichia coli drawn to molecular scale by David Goodsell Cells Contain High Concentrations of Proteins that Participate in a Multitude of Interactions

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Types of Biological Molecular Network Models Models of molecular interaction networks may be constructed at varying levels of resolution: Quantitative models use sets of differential equations to model molecular concentrations Qualitative models use graphs to represent functional relationships between molecules

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Quantitative Model: EGFR Pathway Ordinary differential equations include: 94 state variables 95 parameters From: Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol. 2002 Apr;20(4):370-5.

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Qualitative Model: 2-hybrid yeast protein map 1548 proteins 2358 interactions From: Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nat Biotechnol. 2000 Dec;18(12):1257-61.

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Advantages and Disadvantages of Different Network Models Quantitative: Advantage – allows for quantitative predictions of perturbations on the system Disadvantage – requires an understanding of the network connectivity as well as reaction rates: many parameters must be modeled per interaction. These models are typically constructed for small networks (andlt; 10 proteins) Qualitative: Advantage – requires only the understanding of network connectivity (1 bit per interaction) Disadvantage – leads to qualitative not quantitative hypotheses

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Qualitative Protein Networks May be Reconstructed From Varied Data Direct measurements of protein interactions Automated analysis of literature Analysis of expression microarrays Analysis of co-evolving genes

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Experimental Methods for Detection of Protein-Protein Physical Interactions Physical Interactions: Two hybrid Co-purification Protein Fragment Complementarity Assays Protein Chips

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Database of Interacting Proteins (DIP)

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Properties of Protein Networks

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Number of proteins per network Number of clusters One Subgraph Typically Includes Most Nodes

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Most Nodes are Connected by Short Path Lengths

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Networks are Scale-free

Network Hubs Tend to be Proteins Essential for Survival: 

Network Hubs Tend to be Proteins Essential for Survival

Protein Networks Tend to have High Local Connectivity: 

Protein Networks Tend to have High Local Connectivity Low Connectivity High Connectivity

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Qualitative Protein Networks May be Reconstructed From Varied Data Direct measurements of protein interactions Automated analysis of literature Analysis of expression microarrays Analysis of co-evolving genes

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Applications of Expression Data Analysis Classification of experiments cancer diagnoses Classification of gene relationships find co-expressed genes

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Degree of Correlation The degree of correlation between two genes is computed by the Pearson correlation coefficient:

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Conventional Analysis of Expression Data using Hierarchical clustering

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Challenges for Inferring Gene Relationships from Expression Data Extract correlations between genes from correlated data sets Experiment 1 Experiment 2

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Correlations Between Experiments may be Removed by Transforming Coordinates Experiment 1 Experiment 2 Experiment 1’ Experiment 2’

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Ergosterol Biosyntehsis Pathway in Yeast According to KEGG

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Recovery of Ergosterol Pathway using Yeast Expression Data

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Co-expression Among Ergosterol Genes in Untreated Expression Data

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Co-expression Among Ergosterol Genes in Decorrelated Expression Data

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Benchmarking Co-expression Data

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Treating Rheumatoid Arthritis by Discovering Genes Involved in T cell Activation

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T Cell Receptor (TCR) Signaling The immune response involves the recognition by T lymphocytes of peptide fragments (antigens) derived from foreign pathogens This recognition event is mediated by the T cell receptor (TCR) This signaling cascade leads to the induction of transcription of the IL-2 gene T cell activation is implicated in autoimmune diseases

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T Cell Receptor (TCR) Signaling

Human Network: 

Human Network Links are generated from the analysis of literature, co-evolution and co-expression 200,000 links between 20,000 human proteins Links are 70% accurate in recapitulating known pathway associations 30,000 links between 7000 proteins are supported by multiple methods

Uncharacterized Receptor linked to T Cell Receptor: 

Uncharacterized Receptor linked to T Cell Receptor

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RNAi T cell IL2 Experimental Validation of Computational Predictions Ionomycin (P/I) CD3/28

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Reduction of Receptor X mRNA leads to upregulated IL-2 Production siRNA

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Induced Gene of Interest Following T Cell Activation Jurkat Peripheral CD4+ Cells ______ 30m ______ 30m ______ 2h ______ 4h ______ 8h ______ 4h ______ 8h CD3/28 P/I CD3/28 P/I CD3/28 P/I CD3/28 P/I CD3/28 P/I CD3/28 P/I CD3/28 P/I Receptor X Gene is Induced Following T Cell Activation

Receptor X is a Putative Drug Target: 

Receptor X is a Putative Drug Target Orphan GPCR in rhodopsin family Coexpressed with TCR pathway proteins Target is upregulated upon TCR activation Reduction of target mRNA leads to upregulated IL-2 Production Novel drug would be an agonist to target causing downregulation of TCR activation

Acknowledgements: 

Acknowledgements Michel Thompson Peter Bowers Kelly Oliner Seenu Kothakota Bill Boyle Steve Wickert Joe Fierro Darin Taverna Taruna Arora Marco Vasquez