do not reproduce without permission 1 gerstein.info/talks (c) 2005 1 (c) mark gerstein, 2002, yale,...

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Permissions Statement

This Presentation is copyright Mark Gerstein, Yale University, 2006.

Feel free to use images in it with

PROPER acknowledgement

(via citation to relevant papers or link to gersteinlab.org).

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Understanding Protein Function on a Genome-scale using Networks

Mark B GersteinYale (Comp. Bio. & Bioinformatics)

Orfeome 2006

2006.11.16, 16:30-17:00

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The problem: Grappling with Function on a Genome Scale?

• 250 of ~530 originally characterized on chr. 22 [Dunham et al.]

• >25K Proteins in Entire Human Genome(with alt. splicing)

.…… ~530

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Traditional single molecule way to integrate

evidence & describe function

Descriptive Name: Elongation Factor 2

Summary sentence describing function:

This protein promotes the GTP-dependent

translocation of the nascent protein chain from the A-site to the P-site of

the ribosome.

EF2_YEAST

Lots of references to papers

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Toward Systematic Ontologies for Function,

using Networks

All of SCOP entries

1Oxido-

reductases

3Hydrolases

1.1Acting on CH-OH

1.1.1.1 Alcohol dehydrogenase

ENZYME

1.1.1NAD and

NADP acceptor

NON-ENZYME

3.1Acting on

ester bonds

1 Meta-bolism

1.1 Carb.

metab.

3.8 Extracel.

matrix

3.8.2 Extracel.

matrixglyco-protein

1.1.1 Polysach.

metab.

3.8.2.1 Fibro-nectin

General similarity Functional class similarityPrecise functional similarity

3 Cell

structure

1.5Acting on

CH-NH

3.4Acting on

peptide bonds

1.1.1.3Homoserine

dehydrogenase

1.2Nucleotide

metab.

3.1 Nucleus

3.8.2.2Tenascin

1.1.1.1 Glycogenmetab.

1.1.1.2 Starchmetab.

3.1.1.1 Carboxylesterase

3.1.1Carboxylic

ester hydro-lases

3.1.1.8 Cholineesterase

General Networks[Eisenberg et al.]

Hierarchies & DAGs[Enzyme, Bairoch; GO, Ashburner;

MIPS, Mewes, Frishman]

Interaction Vectors [Lan et al, IEEE 90:1848]

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Outline

• Background Why Study Networks? Interaction Networks and their properties

• 3-D Structural Analysis of Protein Interaction Networks Gives New Insight Into Protein Function, Network Topology and Evolution 3-D structural point of view Network properties revisited

• Genomic analysis of the hierarchical structure of regulatory networks Construction Characteristics

• TopNet & tYNA

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PROTEIN INTERACTION NETWORKS IN YEAST

Source: Gavin et al. Nature (2002), Uetz et al. Nature (2000), Cytoscape and DIP

• Determined by:

– Large-scale Yeast-two-hydrid

– TAP-Tagging

– Literature curation

• Currently over 20,000 unique interactions available in yeast

• Spawned a field of computational “graph theory” analyses that view proteins as “nodes” and interactions as “edges”

A snapshot of the current interactome Description and methodologies

ILLUSTRATIVE

DIP (Database of interacting Proteins)

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INTERESTING PROPERTIES OF INTERACTION NETWORKS

Source: Various, see following slides

Network topology

Network Evolution

Relationship of topology and genomic features

Examples of studies

• What distribution does the degree (number of interaction partners) follow?

• What is the relationship between the degree and a proteins essentiality?

• Is there a relationship between a proteins connectivity and expression profile?

• What is the relationship between a proteins evolutionary rate and its degree?

• How did the observed network topology evolve?

OVERVIEW

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HUBS TEND TO BE IMPORTANT PROTEINS, THEY ARE MORE LIKELY TO BE ESSENTIAL PROTEINS AND TEND TO BE MORE CONSERVED

Source: Jeong et al. Nature (2001), Yu et al. TiG (2004) and Fraser et al. Science (2002)

• By now it is well documented that proteins with a large degree tend to be essential proteins in yeast.

(“Hubs are essential”)

• Likewise, it has been found that hubs tend to evolve more slowly than other proteins

(“Hubs are slower evolving”)

Some Debate on this

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THERE IS A RELATIONSHIP BETWEEN NETWORK TOPOLOGY AND GENE EXPRESSION DYNAMICS

Source: Han et al. Nature (2004) and Yu*, Kim* et al. (Submitted)

Frequency

Co-expression correlation

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Outline




• TopNet & tYNA

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MOTIVATION

≠

AB1-4

Cdk/cyclin complex Part of the RNA-pol complex

ILLUSTRATIVE

A

B1

B2

B3

B4

Network perspective:

Structural biology perspective:

=

There remains a rich sourceof knowledge unmined by network

theorists!

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THERE IS A PROBLEM WITH SCALE-FREENESS AND REALLY BIG HUBS IN INTERACTION NETWORKS

Source: DIP, Institut fuer Festkoerperchemie (Univ. Tuebingen)

A really big hub (>200 Interactions)

Gedankenexperiment

How many maximum neighbors can a protein have?

• Clearly, a protein is very unlikely to have >200 simultaneous interactors.

• Some of the >200 are most likely false positives

• Some others are going to be mutually exclusive interactors (i.e. binding to the same interface).

Conclusion

• There appears to be an obvious discrepancy between >200 and 12.

ILLUSTRATIVEWouldn’t it be great to

be able to see the differentbinding interfaces?

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UTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES

Source: Kim et al. Science (in press)

ILLUSTRATIVE

PDB

Map all interactions to available homologous structures of interfaces

Distinguish overlapping from non-overlapping interfaces

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SHORT DIGRESSION: THIS ALLOWS US TO DISTINGUISH SYSTEMATICALLY BETWEEN SIMULTANEOUSLY POSSIBLE AND MUTUALLY EXCLUSIVE INTERACTIONS

Simultaneouslypossible

interactions

Mutuallyexclusive

interactions


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THAT IS HOW THE RESULTING NETWORK LOOKS LIKE

Source: PDB, Pfam, iPfam and Kim et al. Science (in press)

• Represents a “very high confidence” network

• Total of 873 nodes and 1269 interactions, each of which is structurally characterized

• 438 interactions are classified as mutually exclusive and 831 as simultaneously possible

• While much smaller than DIP, it is of similar size as other high-confidence datasets

The Structural Interaction Network (SIN) Properties

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Outline




• TopNet & tYNA

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REMEMBER THE NETWORK PROPERTIES AS WE DESCRIBED BEFORE?

Source: Various, see following slides

Network topology

Network Evolution

Relationship of topology and genomic features

Examples of studies

• What distribution does the degree (number of interaction partners follow?)

• Does the network easily separate into more than one component?

• What is the relationship between the degree and a proteins essentiality?

• Is there a relationship between a proteins connectivity and expression profile?

• What is the relationship between a proteins evolutionary rate and its degree?

• How did the observed network topology evolve?

OVERVIEW

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THERE DO NOT APPEAR TO BE THE KINDS OF REALLY BIG HUBS AS SEEN BEFORE – IS THE TOPOLOGY STILL SCALE-FREE?


• With the maximum number of interactions at 13, there are no “really big hubs” in this network

• Note that in other high-confidence datasets (or similar size), there are still proteins with a much higher degree

• The degree distribution appears to top out much earlier and less scale free than that of other networks

Degree distribution Properties

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Entire genomeAll proteins

In our dataset

64.9%

31.8%32.3%15.1%

Single-interface hubs only

Multi-interface hubs only

Percentage ofessential proteins

IT’S REALLY ONLY THE MULTI-INTERFACE HUBS THAT ARE SIGNIFICANTLY MORE LIKELY TO BE ESSENTIAL


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All proteinsIn our dataset

Single-interface hubs only

Multi-interface hubs only

ExpressionCorrelation

0.20.17

0.25

Expression correlation

DATE-HUBS AND PARTY-HUBS ARE REALLY SINGLE-INTERFACE AND MULTI-INTERFACE HUBS

Source: Han et al. Nature (2004) and Kim et al. Science (in press)

Frequency

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AND ONLY MULTI-INTERFACE PROTEINS ARE EVOLVING SLOWER, SINGLE-INTERFACE HUBS DO NOT

Entire genomeAll proteins

In our datasetSingle-interface

hubs onlyMulti-interface

hubs only

EvolutionaryRate (dN/dS)

0.029

0.077

0.047 0.051


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IN FACT, EVOLUTIONARY RATE CORRELATES BEST WITH THE FRACTION OF INTERFACE AVAILABLE SURFACE AREA


DATA IN BINS

Small portion of surface area involved in interfaces – fast evolving

Large portion of surface area involved in interfaces – slow evolving

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IS THERE A DIFFERENCE BETWEEN SINGLE-INTERFACE HUBS AND MULTI-INTERFACE HUBS WITH RESPECT TO NETWORK EVOLUTION?


The Duplication Mutation Model In the structural viewpoint

If these models were correct,there would be an enrichment of

paralogs among B

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0.00%

0.15%

0.07%

0.003%

Random pair

Same partner

Same partnerdifferent interface

Same partnersame interface

Fraction of paralogsbetween pairs of proteins

MULTI-INTERFACE HUBS DO NOT APPEAR TO EVOLVE BY A GENE DUPLICATION – THE DUPLICATION MUTATION MODEL CAN ONLY EXPLAIN THE EXISTENCE OF SINGLE-INTERFACE HUBS


But that also means that the duplication-mutation modelcannot explain the full current

interaction network!

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Outline




• TopNet & tYNA

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Target Genes

Transcription Factors

142 transcription factors

3,420 target genes

7,074 regulatory interactions

From integrating data from Snyder, Young, Kepes, and

TRANSFAC

Yeast Regulatory Network: a platform for integration

[Yu et al (2003), TIG]

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Determination of "Level" in Regulatory Network Hierarchy with

Breadth-first Search

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Yeast Regulatory Hierarchy: the Middle-managers Rule

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Example of Path Through Regulatory Network

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Yeast Network Similar in Structure to Government Hierarchy

with Respect to Middle-managers

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Yeast and E. coli Networks similar

in Structure

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Outline




• TopNet & tYNA

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Characteristics of Regulatory Hierarchy: Middle Managers are Information Flow

Bottlenecks

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Characteristics of Regulatory Hierarchy: The Paradox of Influence and Essentiality

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Characteristics of Regulatory Hierarchy:

Topmost proteins sit at center of protein

interaction networkAvg. Closeness

Le

vel

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Outline




• TopNet & tYNA

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TopNet – an automated web tool

[Yu et al., 2004; Yip et al. (2005); Similar tools include Cytoscape.org, Idekar, Sander et al]

(vers. 2 :"TopNet-like

Yale Network Analyzer")

Normal website + Downloaded code (JAVA)+ Web service (SOAP) with Cytoscape plugin

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SVGA visualization, Network Mgt. (Multiple Network Support, tagging with DB)

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• TopNet & tYNA

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Conclusions• 3D Analysis of Interaction Network

The topology of a direct physical interaction network is much less dominated by hubs than previously thought

Several genomic features that were previously thought to be correlated with the degree are in fact related to the number of interfaces and not the degree

Specifically, a proteins evolutionary rate appears to be dependent on the fraction of surface area involved in interactions rather than the degree

The current network growth model can only explain a part of currently known networks

• Regulatory Network Hierarchies Middle managers dominate, sitting at info. bottlenecks Paradox of influence and essentiality Topmost proteins sit at center of interaction network

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MG

MS

AcknowledgementsAcknowledgements

TopNet.GersteinLab.org

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Acknowledgements


MS

MG

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Acknowledgements


MS

MG

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Acknowledgements


MS

MG

H Yu

P KimK Yip

Y Xia

A Paccanaro

J Lu S Douglas

NIH, NSF, Keck

do not reproduce without permission 1 gerstein.info/talks (c) 2005 1 (c) mark gerstein, 2002, yale,...

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