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3-D Structural Analysis of Protein Interaction Networks Gives New Insight Into Protein Function, Network Topology and Evolution. 2006 Telluride Workshop Philip M. Kim, Ph.D., Yale University. New Haven, CT August 14th, 2006. MOTIVATION. ILLUSTRATIVE. Network perspective:. =.

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slide1

3-D Structural Analysis of Protein Interaction Networks Gives New Insight Into Protein Function, Network Topology and Evolution

2006 Telluride Workshop

Philip M. Kim, Ph.D., Yale University

New Haven, CT

August 14th, 2006

motivation
060119_CSB_Talk_PMKMOTIVATION
  • ILLUSTRATIVE

Network perspective:

=

There remains a rich source

of knowledge unmined by network

theorists!

Structural biology perspective:

B4

B1-4

A

B3

B1

B2

A

Part of the RNA-pol complex

Cdk/cyclin complex

outline
060119_CSB_Talk_PMKOUTLINE
  • Interaction Networks and their properties
  • A 3-D structural point of view
  • Network properties revisited
  • Conclusions
outline4
060119_CSB_Talk_PMKOUTLINE
  • Interaction Networks and their properties
  • A 3-D structural point of view
  • Network properties revisited
  • Conclusions
protein interaction networks in yeast
060119_CSB_Talk_PMKPROTEIN INTERACTION NETWORKS IN YEAST
  • ILLUSTRATIVE
  • A snapshot of the current interactome
  • Description and methodologies
  • 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”

DIP (Database of interacting Proteins)

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

tiny glossary degree and hubs
060119_CSB_Talk_PMKTINY GLOSSARY: DEGREE AND HUBS
  • A: Degree = 5
  • A is a “Hub”*
  • C: Degree = 1

* The definition of hubs is somewhat arbitrary, usually a cutoff is used

Source: PMK

interesting properties of interaction networks
060119_CSB_Talk_PMKINTERESTING PROPERTIES OF INTERACTION NETWORKS
  • OVERVIEW
  • Examples of studies
  • What distribution does the degree (number of interaction partners) follow?
  • Network topology
  • Relationship of topology and genomic features
  • 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?
  • Network Evolution
  • How did the observed network topology evolve?

Source: Various, see following slides

interaction networks are scale free their topology is dominated by so called hubs
060119_CSB_Talk_PMKINTERACTION NETWORKS ARE SCALE-FREE – THEIR TOPOLOGY IS DOMINATED BY SO-CALLED HUBS
  • So-called scale-free topology has been observed in many kinds of networks (among them interaction networks)

p(k) ~ kγ

  • Scale freeness: A small number of hubs and a large number of poorly connected ones (“Power-law behavior”)
  • Topology is dominated by “hubs”
  • Scale-freeness is in stark contrast to normal (gaussian) distribution

Source: Barabasi, A. and Albert, R., Science (1999)

slide9
060119_CSB_Talk_PMKHUBS TEND TO BE IMPORTANT PROTEINS, THEY ARE MORE LIKELY TO BE ESSENTIAL PROTEINS AND TEND TO BE MORE CONSERVED
  • 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”)

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

or are they there is an ongoing debate about the relationship between evolutionary rate and degree
060119_CSB_Talk_PMK… OR ARE THEY? THERE IS AN ONGOING DEBATE ABOUT THE RELATIONSHIP BETWEEN EVOLUTIONARY RATE AND DEGREE
  • EXAMPLES
  • No, the relationship is unclear
  • Yes, hubs are more conserved
  • Fraser et al. Science (2002)
  • Jordan et al. Genome Res. (2002)

?

  • Jordan et al. BMC Evol. Biol. (2003)
  • But the “Yes” side appears to be winning
  • Fraser et al. BMC Evol. Biol. (2003)
  • Hahn et al. J. Mol. Evol. (2004)
  • Wuchty Genome Res. (2004)
  • Fraser Nature Genetics (2005)

Source: See text

there is a relationship between network topology and gene expression dynamics
060119_CSB_Talk_PMKTHERE IS A RELATIONSHIP BETWEEN NETWORK TOPOLOGY AND GENE EXPRESSION DYNAMICS

Frequency

Co-expression correlation

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

scale freeness generally evolves through preferential attachment the rich get richer
060119_CSB_Talk_PMKSCALE FREENESS GENERALLY EVOLVES THROUGH PREFERENTIAL ATTACHMENT (THE RICH GET RICHER)
  • ILLUSTRATIVE
  • The Duplication Mutation Model
  • Description
  • Theoretical work shows that a mechanism of preferential attachment leads to a scale-free topology

(“The rich get richer”)

  • In interaction network, gene duplication followed by mutation of the duplicated gene is generally thought to lead to preferential attachment

The interaction partners of A are more likely to be

duplicated

Gene duplication

  • Simple reasoning: The partners of a hub are more likely to be duplicated than the partners of a non-hub

Source: Albert et al. Rev. Mod. Phys. (2002) and Middendorf et al. PNAS (2005)

outline13
060119_CSB_Talk_PMKOUTLINE
  • Interaction Networks and their properties
  • A 3-D structural point of view
  • Network properties revisited
  • Conclusions
there is a problem with scale freeness and really big hubs in interaction networks

Conclusion

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

How many maximum neighbors can a protein have?

060119_CSB_Talk_PMK

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

Wouldn’t it be great tobe able to see the different

binding interfaces?

  • ILLUSTRATIVE
  • A really big hub (>200 Interactions)

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

utilizing protein crystal structures we can distinguish the different binding interfaces

Use a high-confidence

  • filter
  • Homology mapping
  • of Pfam domains
  • to all structures of
  • interactions
  • PDB
  • ~10000 Structures
  • of interactions*

060119_CSB_Talk_PMK

UTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES
  • ILLUSTRATIVE
  • Interactome
  • ~20000 interactions
  • Map Pfam domains to all
  • proteins in the interactome
  • Annotate interactions
  • with available structures,
  • discard all others

Combine with all

structures of yeast

protein complexes

  • Distinguish
  • interfaces

* Many redundant structures

Source: PMK

slide16
060119_CSB_Talk_PMKSHORT DIGRESSION: THIS ALLOWS US TO DISTINGUISH SYSTEMATICALLY BETWEEN SIMULTANEOUSLY POSSIBLE AND MUTUALLY EXCLUSIVE INTERACTIONS

Mutually

exclusive

interactions

Simultaneously

possible

interactions

Source: PMK

slide17
060119_CSB_Talk_PMKSIMULTANEOUSLY POSSIBLE INTERACTIONS (“PERMANENT”) MORE OFTEN LINK PROTEINS THAT ARE FUNCTIONALLY SIMILAR, COEXPRESSED AND CO-LOCATED

Fraction

same

biological

process

Fraction

same

cellular

component

p<<0.001

p<<0.001

Fraction

same

molecular

function

Co-expression

correlation

p<<0.001

p<<0.001

Mutually

exclusive

interactions

Mutually

exclusive

interactions

Simultaneously

possible

interactions

Simultaneously

possible

interactions

Source: PMK

that is how the resulting network looks like
060119_CSB_Talk_PMKTHAT IS HOW THE RESULTING NETWORK LOOKS LIKE
  • The Structural Interaction Dataset (SID)
  • Properties
  • 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

Source: PDB, Pfam, iPfam and PMK

outline19
060119_CSB_Talk_PMKOUTLINE
  • Interaction Networks and their properties
  • A 3-D structural point of view
  • Network properties revisited
  • Conclusions
remember the network properties as we described before
060119_CSB_Talk_PMKREMEMBER THE NETWORK PROPERTIES AS WE DESCRIBED BEFORE?
  • OVERVIEW
  • Examples of studies
  • What distribution does the degree (number of interaction partners follow?)
  • Does the network easily separate into more than one component?
  • Network topology
  • Relationship of topology and genomic features
  • 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?
  • Network Evolution
  • How did the observed network topology evolve?

Source: Various, see following slides

slide21
060119_CSB_Talk_PMKTHERE DO NOT APPEAR TO BE THE KINDS OF REALLY BIG HUBS AS SEEN BEFORE – IS THE TOPOLOGY STILL SCALE-FREE?
  • Degree distribution
  • Properties
  • 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

Source: PMK

it s really only the multi interface hubs that are significantly more likely to be essential
060119_CSB_Talk_PMKIT’S REALLY ONLY THE MULTI-INTERFACE HUBS THAT ARE SIGNIFICANTLY MORE LIKELY TO BE ESSENTIAL

Percentage of

essential proteins

Single-interface

hubs only

All proteins

In our dataset

Multi-interface

hubs only

Entire genome

Source: PMK

date hubs and party hubs are really single interface and multi interface hubs
060119_CSB_Talk_PMKDATE-HUBS AND PARTY-HUBS ARE REALLY SINGLE-INTERFACE AND MULTI-INTERFACE HUBS

Frequency

Expression correlation

Expression

Correlation

Single-interface

hubs only

All proteins

In our dataset

Multi-interface

hubs only

Source: Han et al. Nature (2004) and PMK

and only multi interface proteins are evolving slower single interface hubs do not
060119_CSB_Talk_PMKAND ONLY MULTI-INTERFACE PROTEINS ARE EVOLVING SLOWER, SINGLE-INTERFACE HUBS DO NOT

Evolutionary

Rate (dN/dS)

Single-interface

hubs only

All proteins

In our dataset

Multi-interface

hubs only

Entire genome

Source: PMK

or are they there is an ongoing debate about the relationship between evolutionary rate and degree25
060119_CSB_Talk_PMK… OR ARE THEY? THERE IS AN ONGOING DEBATE ABOUT THE RELATIONSHIP BETWEEN EVOLUTIONARY RATE AND DEGREE
  • No, the relationship is unclear
  • Yes, hubs are more conserved

This debate may have arisen

because the two different sides were

all looking at the wrong variable!

  • Fraser et al. Science (2002)
  • Jordan et al. Genome Res. (2002)

?

  • Jordan et al. BMC Evol. Biol. (2003)
  • But the “Yes” side appears to be winning
  • Fraser et al. BMC Evol. Biol. (2003)
  • Hahn et al. J. Mol. Evol. (2004)
  • Wuchty Genome Res. (2004)

Source: See text

in fact evolutionary rate correlates best with the fraction of interface available surface area
060119_CSB_Talk_PMKIN FACT, EVOLUTIONARY RATE CORRELATES BEST WITH THE FRACTION OF INTERFACE AVAILABLE SURFACE AREA
  • DATA IN BINS

Large portion of surface area involved in interfaces – slow evolving

Small portion of surface area involved in interfaces – fast evolving

Source: PMK

slide27
060119_CSB_Talk_PMKIS THERE A DIFFERENCE BETWEEN SINGLE-INTERFACE HUBS AND MULTI-INTERFACE HUBS WITH RESPECT TO NETWORK EVOLUTION?
  • The Duplication Mutation Model
  • In the structural viewpoint

The interaction partners of A are more likely to be

duplicated

Gene duplication

If these models were correct,

there would be an enrichment of

paralogs among B

Source: PMK

slide28
060119_CSB_Talk_PMK

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 model

cannot explain the full current

interaction network!

Fraction of paralogs

between pairs of proteins

Random

pair

Same

partner

Same

partner

different

interface

Same

partner

same

interface

Source: PMK

outline29
060119_CSB_Talk_PMKOUTLINE
  • Interaction Networks and their properties
  • A 3-D structural point of view
  • Network properties revisited
  • Conclusions
conclusions
060119_CSB_Talk_PMKCONCLUSIONS
  • PRELIMINARY
  • 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

Source: PMK

acknowledgements
060119_CSB_Talk_PMKACKNOWLEDGEMENTS

Mark Gerstein

Long Jason Lu

Yu Brandon Xia

The Gersteinlab, in particular:

Alberto Paccanaro

Jan Korbel

Joel Rozowsky

Tara Gianoulis

Tom Royce

utilizing protein crystal structures we can distinguish the different binding interfaces34
060119_CSB_Talk_PMKUTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES
  • ILLUSTRATIVE
  • Start with high-confidence interactome dataset
  • Collected dimer and multimer structures and mapped Pfam domains onto the corresponding proteins
  • Removed ubiquitous domains (e.g., WD40)
  • All interactions that contain Pfam domains found to interact in a crystal structure are annotated with this structural information (all others are removed)
  • Dataset: ~1269 interactions (combined with all structures that were from yeast).

Pfam -- Homology

Combine with all

structures of yeast

protein complexes

Explain methodology….

Source: PMK

utilizing protein crystal structures we can distinguish the different binding interfaces35
060119_CSB_Talk_PMKUTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES
  • ILLUSTRATIVE
  • Interactome
  • Use a high-confidence
  • filter
  • ~20000 interactions
  • Map Pfam domains to all
  • proteins in the interactome
  • Homology mapping
  • of Pfam domains
  • to all structures of
  • interactions
  • PDB
  • Annotate interactions
  • with available structures,
  • discard all others
  • ~10000 Structures
  • of interactions*

Combine with all

structures of yeast

protein complexes

  • Distinguish
  • interfaces

* Many redundant structures

Source: PMK

some network statistics scale freeness
060119_CSB_Talk_PMKSOME NETWORK STATISTICS – SCALE FREENESS?
  • In the Pfam dataset, the vast majority (570 out of 790) of the proteins (even hubs) has only one distinct interface.
  • 220 proteins (~25%) have 2 or more interfaces.
  • Most hubs are mediated by promiscuous interfaces rather than many interfaces ~ 2.6 interactions/interface

Max

Degree

161 nodes

(degree >5)

Avg.

Degree

Max

Interfaces

220 nodes

(numint>1)

Avg.

Interfaces

Source: PMK

utilizing protein crystal structures we can distinguish the different binding interfaces37
060119_CSB_Talk_PMKUTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES
  • ILLUSTRATIVE
  • Interactome
  • PDB

Source: PMK

environmental effects on organizational structure
060119_CSB_Talk_PMKENVIRONMENTAL EFFECTS ON ORGANIZATIONAL STRUCTURE

* …

Source: …

five different organizational configurations

060119_CSB_Talk_PMK

FIVE DIFFERENT ORGANIZATIONAL CONFIGURATIONS

* …

Source: …