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Announcement

0. 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. Analysis of Protein-protein interaction networks :. towards functional classifications

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Announcement

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  2. 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

  3. Analysis of Protein-protein interaction networks : towards functional classifications of proteomes TAG 2006 Annecy Bernard Jacq IBDML Marseille

  4. Analysis of Protein-protein interaction networks : towards functional classifications of proteomes TAG 2006 Annecy Bernard Jacq IBDML Marseille

  5. • 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 Uetz Trends in Cell Biology (2001), 11, 102-105 • 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. Yeates Nature (2000), 405, 823-826

  6. 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)

  7. 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)

  8. Multi-disciplinary approaches to study protein function From Ognjenka Goga Vukmirovic & Shirley M. Tilghman Nature (2000), 405, 820-822

  9. Structure and function are the yin and the yang of biology Yin Protein 3D structure Yang Function : toxin, kills the cell

  10. The study of relationships between structure and function in biology is a very old question The Causal Relations between Structure and Function in Biology E. Stanley Abbot American Journal of Psychology, Vol. 27, No. 2 (Apr., 1916) , pp. 245-250

  11. Structure and function of biological objects StructureFunction The kidney filter wastes (especially urea) from the blood and excrete them, along with water, as urine 1/size Organ : kidney Specialized cell of the kidney, involved in blood filtering Cell : tubular epithelial cell An ion channel is an an assembly of several integral membrane proteins which permit the passage of ions through the membrane molecule : ion channel

  12. WHAT ARE PROTEIN STRUCTURE AND PROTEIN FUNCTION ? STRUCTURE • 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)

  13. WHAT ARE PROTEIN STRUCTURE AND PROTEIN FUNCTION ? 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.

  14. It is essential to distinguish different functional levels BIOCHEMICAL FUNCTION Molecular activity of the gene product Examples : ATPase, DNA-binding protein … CELLULAR FUNCTION Cellular process in which the gene product is involved  integration of the biochemical function within a given process Examples : DNA synthesis, nucleotide metabolism, protein trafic .....

  15. EXAMPLE : THE FUNCTIONS OF THE YEAST RAP1 PROTEIN Biochemical functions : • Transcription factors • DNA-binding protein Cellular functions : • RNA polymerase IIdependant transcription • chromatin/chromosome structure • Carbohydrate Metabolism There are more than two possible functional levels

  16. Different levels of functional integration Structural levels Functional levels integration Inter-species relationships, Ecological Equilibria Populations Development, reproduction, aging Organisms Tissues,organs Physiological regulations Migrations, Communications Cells Interaction networks betweenmolecules Pathways Biochemical function Molecule

  17. Structural levels Functional levels integration Inter-species relationships, Ecological Equilibria Populations Development, reproduction, aging Organisms Tissues,organs Physiological regulations Biochemical function Molecule Protein function can be defined at many structural levels Migrations, Communications Cells Interaction networks betweenmolecules Pathways

  18. 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)

  19. 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)

  20. 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 ?

  21. 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)

  22. Functional prediction methods which make use of genomic data

  23. 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)

  24. Combined methods for functional predictions Marcotte et al., Nature 402, 83-6 (1999)

  25. Example of a network of functional links between proteins Nature 402, 83-6 (1999)

  26. Functional inference methods using correlations in genomic data : Summary • 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)

  27. 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)

  28. 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 !

  29. Structure Genome Transcriptome Functional predictions: Sequence Classical approaches Function New, Genomic approaches Interactome Proteome

  30. 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

  31. HOW TO ANALYZE INTERACTION NETWORKS ? SOME BIOLOGICAL QUESTIONS ASKED FROM A GRAPH THEORY POINT OF VIEW

  32. What can be inferred about the functional relationships between A and B on the one hand and C and D on the other ? A B C D Tucker, Gera and Uetz Trends in Cell Biology, March 2001 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

  33. Development of a new functional classification method (ProDistIn) The central idea : Do not compare proteins themselves but… … compare the lists of their interactors…

  34. The PRODISTIN method : Objectives and approach • 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 : Interaction graph  distance matrix Class identification (topology, GO Biological Process annotations) Classification tree Annotated tree

  35. Z 1- Czekanovski-Dice distance for protein pairs i j k l m X spec + Y spec 1 + 4 D(X, Y) = = = 0.45 Y 8 + 3 (X UY) + (X  Y) X c a b f g h In order to make a functional comparison between N proteins: - calculate D for all pairwise comparisons of proteins - fill in a distance matrix e X Y Z T d X - 0.45 0.5 0.77 Y - 0.6 0.66 Z - 0.84 n o p T - X T Y Z T ProDistIn : the 3 first steps 2- distance table for all possible pairs Apply a clusterisation method (e.g. NJ) and build a functional similarity tree 3- clusterisation and tree drawing

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

  37. RESULT : FUNCTIONAL PROXIMITY TREE FOR 602 YEAST PROTEINS

  38. Splicing RNA Degradation RNA MATURATION SUBTREE

  39. Splicing Degradation RNA METABOLISM GREAT TREE ?

  40. RNA METABOLISM GREAT TREE Maturation 3’ extremity Translation Splicing RNA Degradation

  41. Cell cycle Cell cycle Cell cycle 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

  42. 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)

  43. Study of 9 Drosophila developmental signaling pathways from the interactome perspective Objectives and approach • 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)

  44. TOR-RAS2 SEV-RAS2-INS1 INS2 HH2-EGF FGF TGF1 TOL1 TOL3 TGF2 TOL2 WG2 WG3-N2-TGF3 WG1-N1 HH1 A surprising result ! • Pathways are not clustered together • Each pathway is split in two to three Classes (modules) • Proteins from different pathways are often found in the same functional classes

  45. Signaling pathways split Example: the Wnt pathway wg Wnt2 fz fz2 dsh Apc2 Axn sgg CkIa arm gro

  46. Functional classes localization HH2 EGF SEV RAS2 INS1 Membrane TOR RAS1 WG1 N1 TOL1 TGF1 WG2 Cyto HH1 TOL2 TGF2 INS2 N2 WG3 TGF3 Nucleus Localization surepresentation, corroborated by Molecular Functions Polarization of signaling pathway modules

  47. The functional classification allows to propose the involvement of new partners in signaling pathways wg Wnt2 Fz2 Fz pk Vang Dsh dlg1 raps Dsh Drosophila wg pathway new putative players Axn Apc2 sgg arm CG3402 CkIalpha Cytoplasm SH3PX1 Proteins of the 'canonical' pathway arm gro Nucleus pan nej mus309 Prediction of involvement

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