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Announcement

• JOBIM007, the next French Bioinformatics meeting will be held in Marseille in early july 2007

• The official announcement will be made through the bioinfo mailing list in December 2006

Bernard Jacq

IBDML MarseilleTAG 2006Annecy

Analysis of Protein-protein interaction networks :

towards functional classificationsof proteomes

Bernard JacqIBDML Marseille

TAG 2006Annecy

Analysis of Protein-protein interaction networks :

towards functional classificationsof proteomes

• Often it is possible to understand the cellular functions of uncharacterized proteins through their linkages to characterized proteins. In broader terms, the networks of linkages offer a new view of the meaning of protein function, and in time should offer a deepened understanding of the functioning of cells.

David Eisenberg, Edward M. Marcotte, Ioannis Xenarios & Todd O. YeatesNature (2000), 405, 823-826

• A complete understanding of protein functionality will require information on many levels: knowledge of transcriptional, translational and posttranslational regulation, binding constants, structures, protein interactions and cellular networking …..

Chandra L. Tucker, Joseph F. Gera and Peter UetzTrends in Cell Biology (2001), 11, 102-105

Summary

• The notion of protein function(s) and its complicated relationships with protein structure

• Bioinformatics approaches to the study of protein function(s)

• The Bioinformatics study of protein-protein interaction networks as a powerful way to study, predict and compare protein function(s)

Summary

• The notion of protein function(s) and its complicated relationships with protein structure

• Bioinformatics approaches to the study of protein function(s)

• The Bioinformatics study of protein-protein interaction networks as a powerful way to study, predict and compare protein function(s)

From Ognjenka Goga Vukmirovic & Shirley M. TilghmanNature (2000), 405, 820-822

Multi-disciplinary approaches to study protein function

Structure and function are the yin and the yang of biology

Function : toxin,kills the cell

Yin

Yang

Protein 3D structure

The Causal Relations between Structure and Function in BiologyE. Stanley AbbotAmerican Journal of Psychology, Vol. 27, No. 2 (Apr., 1916) , pp. 245-250

The study of relationships between structure and function in biology is a very old question

Structure and function of biological objects

1/size Organ : kidney

Cell : tubular epithelial cell

molecule : ion channel

The kidney filter wastes (especially urea) from the blood and excrete them, along with water, as urine

An ion channel is an an assembly of several integral membrane proteins which permit the passage of ions through the membrane

Specialized cell of the kidney, involved in blood filtering

Structure Function

WHAT ARE PROTEIN STRUCTUREAND PROTEIN FUNCTION ?

• Proteins (amino-acid chains) fold in a specific manner in the 3D space, thus adopting unique shapes.

• The structure of a protein corresponds to a representation of this physical object (primary, secondary, ternary and quaternary structures)

• Even if this object is too smal to be seen directly (or under any microscope) visible, we have a very precise idea of its shape and organisation, thanks to X-Ray or NMR techniques.

• Each structural type (primary, secondary,…) of a given protein can be described precisely and unambiguously, allowing its computational manipulation (e.g. a primary structure is described using a string chain made of 20 possible characters only)

STRUCTURE

WHAT ARE PROTEIN STRUCTUREAND PROTEIN FUNCTION ?

• The function(s) of a protein corresponds to the effector properties of the structure, at different biological levels described thereafter.

• In contrary to the case of protein structures, there is no unique and non-ambiguous way to describe protein function

• This situation has precluded the use of bioinformatics in studying protein function … until a recent period.

FUNCTION

BIOCHEMICAL FUNCTIONMolecular activity of the gene product

Examples : ATPase, DNA-binding protein …

CELLULAR FUNCTIONCellular process in which the gene product is

involved integration of the biochemical function within a given process

Examples : DNA synthesis, nucleotide metabolism, protein trafic .....

It is essential to distinguish

different functional levels

Biochemical functions : Transcription factors DNA-binding protein

Cellular functions : RNA polymerase II dependant transcription chromatin/chromosome structure Carbohydrate Metabolism

EXAMPLE : THE FUNCTIONS OF THE YEAST RAP1 PROTEIN

There are more than two possible functional levels

Structural levels Functional levels

Different levels of functional integration

integration

Molecule Biochemical function

PathwaysInteraction networksbetween molecules

Cells

Tissues, organs

Organisms

Populations

Physiological regulations

Development, reproduction,aging

Inter-species relationships, Ecological Equilibria

Migrations,Communications

Structural levels Functional levels

Protein function can be defined at many structural levels

integration

Molecule Biochemical function

PathwaysInteraction networksbetween molecules

Cells

Tissues, organs

Organisms

Populations

Physiological regulations

Development, reproduction,aging

Inter-species relationships, Ecological Equilibria

Migrations,Communications

Protein function : a complex notion

• A function has to be defined in the context of a structural level

• A protein can have different functions, either within one given structural level and/or at different structural levels

• Necessity of a common language to describe function in different organisms : the GO initiative (Gene Ontology)

Summary

• The notion of protein function(s) and its complicated relationships with protein structure

• Bioinformatics approaches to the study of protein function(s)

• The Bioinformatics study of protein-protein interaction networks as a powerful way to study, predict and compare protein function(s)

How can we represent the function of a protein in a

computer ?• Description of a function with sentences (free text)• Keywords• Ontologies (EC Numbers, GO)

• Use raw, functional data :- expression data (in situ hybridisation,

microarrays)- data from protein complexes- binary interaction data (PP, P-DNA)

• Other, new ways ?

What would we like to do with computer representations of

protein function?

• Describe protein function :• be able to do it at different granularity levels

• Compare function(s)• for different proteins of a same organism• for the « same » proteins of different organisms• for different proteins of different organisms

• Predict function(s)

Functional prediction methods which make use of

genomic data

Genomic functional prediction methods

Inferences by correlation

Gene organisation conservation between organisms Rosetta Stone method (Marcotte et al. (1999),

Science 285, 751-753)

Gene order conservation between organisms Neighbour genes method (Dandekar et al. (1998)

TIBS 23, 324-328; Overbeek et al. (1999) PNAS 96, 2896-2901)

Qualitative gene content variations between organisms Phylogenetic profiles method (Pellegrini et al.

(1999) PNAS 96,4285-4288)

Marcotte et al.,Nature 402, 83-6 (1999)

Combined methods for

functional predictions

Nature 402, 83-6 (1999)

Example of a network

of functional

links between proteins

Propose the likely existence of functional links between proteins

These functional links suggest : that the corresponding proteins participate in a same cellular processus same or related cellular function

that there possibly exist direct interactions between these proteins (protein-protein interactions or protein-DNA) or indirect ones (protein complexes, genetic interactions)

Functional inference methods using correlations in genomic data :

Summary

Summary

• The notion of protein function(s) and its complicated relationships with protein structure

• Bioinformatics approaches to the study of protein function(s)

• The Bioinformatics study of protein-protein interaction networks as a powerful way to study, predict and compare protein function(s)

Limitations of genomic functional prediction methods

• Are often based upon inferences making use of structural data (sequence alignments, domain fusions, gene neighbors, phylogenetic profiles)• Sequence/structure similarity does not always mean functional similarity• Very often, these methods can be applied to a subset of a proteome only (e.g. rosetta stone method)• Are very dependant of annotation quality• Usually need a complete genomic sequence • Problems with automatised annotation transfer between proteins (transitive catastrophes)• None of these methods give access to cellular (or upper level) functional predictions; predictions usually remain at the biochemical level

• NB: In any case, a prediction has always to be experimentally verified !

StructureSequence

Function

Functional predictions:

Transcriptome Proteome Interactome

Genome

Classical approachesNew, Genomic approaches

THE PROTEIN-PROTEIN INTERACTION NETWORK

A PPI NETWORK CAN BE REPRESENTED BY A NON-ORIENTED GRAPH IN WHICH NODES REPRESENT PROTEINS AND EDGES

THE PHYSICAL INTERACTIONS BETWEEN THEM

HOW TO ANALYZE INTERACTION NETWORKS ?

SOME BIOLOGICAL QUESTIONS ASKED FROM A GRAPH THEORY POINT OF VIEW

Biology Graph theory

The largest group of interacting proteins?

The largest connected component of the graph

Proteins participating to the same complex ?

The quasi-cliques

Proteins involved in the same biological process ?

Classification and comparison based on the cellular

function ?

Extraction of graph node classes using classification

methods

Tucker, Gera and Uetz

Trends in Cell Biology, March 2001

AB

DC

What can be inferred about the functional relationships between A and B on the one hand and C and D on the

other ?

C and D interact directly and share several common interactors, whereas A and B do not

It is likely that the network (cellular) functions of C and D are related whereas that of proteins A and B are not

Development of a new functional classification

method (ProDistIn)

The central idea :

Do not compare proteins themselves but…

… compare the lists of their interactors…

• Aim : Develop a new method able to extract functional informations from the structure of a complex network; visualise it in an intuitive way

• Hypothesis : for any two proteins : - many common interaction partners => related functions- few or no common partners => unrelated function

• Approach :

The PRODISTIN method : Objectives and approach

Interaction graph distance matrix-T

0.8-Z

0.60.6-Y

0.70.50.4-X

TZYX

-T

0.8-Z

0.60.6-Y

0.70.50.4-X

TZYX

Classification tree

Class identification(topology, GO Biological

Process annotations)

Annotated tree

1- Czekanovski-Dice distance for protein pairs

e

c a

b

fgh

Y

d

XD(X, Y) = X spec + Y spec

(X U Y) + (X Y) 1 + 48 + 3

= 0.45 =

-T

0.84-Z

0.660.6-Y

0.770.50.45-X

TZYX

2- distance table for all possible pairs

ijklm

Z

T

nop

In order to make a functional comparison between N proteins:

- calculate D for all pairwise comparisons of proteins

- fill in a distance matrix

XYZT

3- clusterisation and tree drawing

Apply a clusterisation method (e.g. NJ) and

build a functional similarity tree

ProDistIn : the 3 first steps

Test on the yeast proteome• A total of 2946 direct protein-protein interactions

involving 2143 proteins• Only proteins with at least 3 interactors are

considered further• => Classification of 602 yeast proteins (10% of

the proteome)

• Double-hybrid screens (Fromont-Racine et al., Uetz et al., Ito et al.)• literature (via MIPS and YPD)• Information Extraction on Medline yeast abstracts

Data from :

RESULT :

FUNCTIONAL

PROXIMITY

TREE

FOR 602

YEAST

PROTEINS

Splicing

RNA MATURATION SUBTREE

RNADegradation

RNA METABOLISM GREAT TREE

Degradation

Splicing

?

Maturation3’ extremity

Translation

RNADegradation

Splicing

RNA METABOLISM GREAT TREE

Main conclusions

• Results correlate very well with current functional knowledge• Statistically robust• Allows prediction of protein function • Prediction of new functional groups • Provides an integrated functional view of a proteome

Publication (highly accessed): Brun, Martin, Chevenet, Guénoche, Jacq, Genome Biol. 2003

Cell cycleCell cycleCell cycle

Since its establishment, ProDistIn has already been used to :– Study functinal classes and make functional predictions

on more than 200 yeast proteins– Study the evolutionary fate of yeast genes originating

from an ancient genome duplication– Study the relationship between sequence similarity

and cellular function similarity– Study the main Drosophila signaling pathways in a

general PPI context– Study the human interactome (under way)– Study the interaction of viruses proteins on the human

proteome (under way)

• Aim : • Study Drosophila signaling pathways in the context of the cell

proteome : how are they organised ?• Propose the existence of new players in several classical

pathways

• Approach : • Constitute high-quality binary PP interaction lists for

Drosophila• Perform PRODISTIN classifications• Other types of bioinformatic analyses to analyse

communications (between pathways and with the rest of the interactome)

Study of 9 Drosophila developmental signaling pathways from the interactome perspective

Objectives and approach

A surprising result !

TOR-RAS2

WG2

HH1WG1-N1

TOL3TGF2 TOL1

INS2 SEV-RAS2-INS1

HH2-EGF

TGF1

TOL2

WG3-N2-TGF3

FGF • Pathways are not clustered together

• Each pathway is split in two to threeClasses (modules)

• Proteins from different pathwaysare often found in thesame functional classes

gro

Wnt2wg

fz fz2dsh

sgg

Axn

CkIa

arm

Apc2

Example: the Wnt pathway

Signaling pathways split

Mem

bran

eC

yto

Nuc

leus

TGF1

TGF2

TOL1

TOL2

INS2

N2WG3TGF3

WG2

HH1

WG1 N1SEV RAS2

INS1 TORRAS1

HH2 EGF

TOL TGF NOTCH WINGLESS HEDGEHOG EGF SEVENLESS INSULIN TORSO

Localization surepresentation, corroborated by Molecular FunctionsPolarization of signaling pathway modules

Functional classes localization

Drosophila wg pathwaynew putative

players

Dsh

armApc2Axn

sggCkIalpha

armpan nej

gro

wgWnt2

DshFz2 Fz

Proteins of the 'canonical' pathway

CG3402

dlg1raps

pk Vang

mus309

SH3PX1

Prediction of involvement

The functional classification allows to propose the involvement of new partners in signaling

pathways

Cytoplasm

Nucleus

Main conclusions

Publication: in preparation

- An alternative view of signal transduction :from linear signalling pathways to a modular, integrated signaling network

- Seems to be true for humans

- More communications within the signaling network than that with the rest of the interactome

- prediction of new components/regulators ? - in the Hedgehog pathway --> experimentally tested in P. Thérond's lab (Nice) - in the Wnt pathway --> discussion with R DasGupta (former Perrimon), NYU : predicted components observed in RNAi screens ?

Study of 9 Drosophila developmental signaling pathways from the interactome perspective

-The PRODISTIN method has been automatised and is now accessible to the community through the Webdistin server

- We have developed another graph analysis method to find functional classes based on the density of edges

Publication : Brun et al, BMC Bioinformatics (2004)

- Development of a method which uses weighted graphs. will allow to integrate other types of data (genetic interactions, transcriptome) by weighting the edges of a protein interaction graph (in preparation)

- Adaptation of PRODISTIN to Protein-DNA networks (classification of both proteins and genes)

Recent developments, Projects

Publication : Baudot et al, Bioinformatics (2006)

A change in our view of protein function

Classical view

The function of protein A is defined by

its action on the transformation of substrate (S) into product (P)

S (Substrate)

P (Product)

A

New perspective

A

The function of protein A is defined by

the context of its interactions with other products in the cell

Adapted from Eisenberg et al, Nature (2000)

Bioinformatics of Interactions and Regulations in Develomentgroup

Present Members :

• Anaïs Baudot (PhD Student)

• Christine Brun (Chargée de recherche)

• Carl Herrmann (Assistant Professor)

• Bernard Jacq  (Research director)

• Pierre Mouren (Software Engeneer)

• Loredana Martignetti (PhD Student)

• Wissem Souiai (PhD Student)

• Delphine Pothier (M2 Student)

Previous Members (2002-2006)e

• Claudine Chaouyia (Assistant Professor)

• Aitor Gonzalez (PhD Student)

• Magali Lescot (Post-Doc)

• David Martin (PhD Student)

• Denis Thieffry (Professor)

+ 8 summer students

Collaboration: Alain Guénoche (IML)

How can we experimentally discover the function of a new gene/protein ?

1-The « classical approach »

Mutant Phenotype

Sequencing,structure

Functional tests

Gene cloning(one gene)

Proposal of a biochemicaland a cellular function onthe basis of experiments

Inferred biochemical function

From a gene-centeredapproach ……Genetical analysis

Molecular Biology

Molecular Biology,Bioinformatics

Genetical analysis, Biochemistry,

Molecular Biology

genes/proteins are the

elementary components of a system,the variations of

which are being studied. Determination of

cellular functionand access

to high levels of functionintegration

Functional Genomics and

Proteomics, Bioinformatics

The approach is changing, so the way of thinking should also change…

How can we discover the function of a new gene/protein ?

2- The « genomic approach »….Towards

Systems biology

The « Rosetta stone » method

Principle : makes use of gene organisation conservation/differences between organisms and of the modularity of proteins

If, in genome 1, gene A is composed of module alpha et gene B composed of module beta only,

If in genome 2, module alpha and module beta are found associated to build only one gene C

Then A et B could be functionally related genes/proteins.

Marcotte et al., Science 285, 751-753 (1999)

What information, brought about by genomics, can be used to develop

new functional prediction methods ?

WITHIN ONE ORGANISMPutative exons, introns, splice sites …. Presence of regulatory sequences near genes (promoters, enhancers ....)Gene content of an organism

BETWEEN ORGANISMSSequence variation/conservation between organisms Qualitative gene content variations between organisms Gene order conservation between organisms Gene organisation conservation between organisms

Marcotte et al., Science 285, 751-753 (1999)

« Rosetta Stone » method :examples

Principle : makes use of the variation of gene (or group of genes) order on the chromosomes

Dandekar et al. TIBS 1998Overbeek et al. PNAS 1999

ABC

Genome 1

AC

B

Genome 2

ABC

Genome 3

ABC

Genome 4

genes & are functionally relatedA B

Neighbour genes method

d

e

BB, Borrelia burgdorferi; DR, Deinococcus radiodurans; CA, Clostridium acetobutylicum; BS, Bacillus subtilis; EF, Enterococcus faecalis; MP, Mycoplasma pneumoniae; MG, Mycoplasma genitalium; ML, Mycobacterium leprae; MT, Mycobacterium tuberculosis; CJ, Campylobacter jejuni; TP, Treponema pallidum; HP, Helicobacter pylori; ST, Streptococcus pyogenes; PN, Streptococcus pneumoniae.

Example : Gene functional groups in glycolysis

Overbeek et al. (1999) PNAS 96, 2896-2901

Pellegrini et al. PNAS 96, 4285-4288 (1999)

Phylogeneticprofiles method

Principle : makes use of correlations (+ ou -) in the qualitative variation of gene content between different organisms

IBDML :

• Christine Brun

• Anais Baudot

• Bernard Jacq

• Wissem Souiai

Collaborations :

• A. Guénoche, IML

• F. Chevenet (Mtpllier)

• 1- Development of PRODISTIN, a generic method for functional classification of members (proteins, genes) of an interaction network

• 2- Application to the study of the functional evolutionary fate of yeast duplicated genes

• 3- Study of 9 Drosophila developmental signaling pathways from an interactome perspective

Structure andanalysis of protein-protein interaction networks

How can we extend individual functional predictions to a complete interaction

network ? >>> functional clusterisation

Example of the Prodistin method (PROtein DIStance based on INteractions

Brun et al., Genome Biology(2003) R, R6

The use of “complete” data changes everything

In classical molecular biology, the main problem is to try to bring a functional answer concerning one gene without studying nearly all other genes (99,9% of the genes)

In genomics, the cleverness is to imagine what you can do when you « see » all the genes (or a majority of them)

It is therefore necessary to change the way of thinking about genes (group of genes, modules...)

Projects - Take advantage of the knowledge gained in the study of Drosophila developmental pathways to study the PI3K vertebrate pathway :

- analyse the modular organisation of the PI3K pathway - continuation of the constitution of a human interactome from the literature

(Internote tool + human validation)- from this analysis, propose new potential components/regulators of

this pathway (will be submitted to experimental validations)- Evolutionary study of the PI3K pathway

Collaboration with E. Goillot’s (Lyon) and C. Brochier’s (Marseille) groups

- Use our human PPI list and newly obtained Y2H virus-human PPIs to study host-viruses interactions

Collaboration with V. Lotteau’s group (Lyon)

Study of 9 Drosophila developmental signaling pathways from the interactome perspective