Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions

Transcription

1 Exploiting Indirect eighbors and Topological Weight to Predict Protein Fnction from Protein-Protein Interactions Limsoon Wong Joint ork ith Hon ian Cha & Wing-Kin Sng Keynote lectre for BioDM 006

2 Protein Fnction Prediction Approaches Seqence alignment (e.g. BLAST) Generatie domain modeling (e.g. HMMPFAM) Discriminatie approaches (e.g. SVM-PAIRWISE) Phylogenetic profiling Sbcelllar co-localization (e.g. PROTFU) Gene expression co-relation Protein-protein interaction Keynote lectre for BioDM 006

3 Protein Interaction Based Approaches eighbor conting (Schikoski et al 000) Rank fnction based on freq in interaction partners Chi-sqare (Hishigaki et al 001) Chi sqare statistics sing expected freq of fnctions in interaction partners Marko Random Fields (Deng et al 003; Letosky et al 003) Belief propagation exploit nannotated proteins for prediction Simlated Annealing (Vazqez et al 003) Global optimization by simlated annealing Exploit nannotated proteins for prediction Clstering (Brn et al 003; Samanta et al 003) Fnctional distance deried from shared interaction partners Clsters based on fnctional distance represent proteins ith similar fnctions Fnctional Flo (abiea et al 004) Assign reliability to arios expt sorces Fnction flos to neighbor based on reliability of interaction and potential Keynote lectre for BioDM 006

4 Fnctional Association Thr Interactions Direct fnctional association: Interaction partners of a protein are likely to share fnctions / it Proteins from the same pathays are likely to interact Indirect fnctional association Proteins that share interaction partners ith a protein may also likely to share fnctions / it Proteins that hae common biochemical physical properties and/or sbcelllar localization are likely to bind to the same proteins Leel-1 neighbor Leel- neighbor Keynote lectre for BioDM 006

5 An illstratie Case of Indirect Fnctional Association? SH3 Proteins SH3-Binding Proteins Is indirect fnctional association plasible? Is it fond often in real interaction data? Can it be sed to improe protein fnction prediction from protein interaction data? Keynote lectre for BioDM 006

6 Materials Protein interaction data from General Repository for Interaction Datasets (GRID) Data from pblished large-scale interaction datasets and crated interactions from literatre niqe and 1839 total interactions Incldes most interactions from the Biomoleclar Interaction etork (BID) and the Mnich Information Center for Protein Seqences (MIPS) Fnctional annotation (FnCat.0) from Comprehensie Yeast Genome Database (CYGD) at MIPS 473 Fnctional Classes in hierarchical order Keynote lectre for BioDM 006

7 Validation Methods Informatie Fnctional Classes Adopted from Zho et al 1999 Select fnctional classes / at least 30 members no child fnctional class / at least 30 members Leae-One-Ot Cross Validation Each protein ith annotated fnction is predicted sing all other proteins in the dataset Keynote lectre for BioDM 006

8 Freq of Indirect Fnctional Association YAL01W YPL088W YJR091C YBR93W YMR300C YPL149W YBL07C YBR055C YDR158W YMR101C 4.1 YBR03C YOR31C YLR330W YBL061C YKL006W YPL193W YLR140W YDL081C YMR047C YDR091C YPL013C Sorce: Kenny Cha Keynote lectre for BioDM 006

9 Oer-Rep of Fnctions in eighbors Fnctional Similarity: S( i j) = F F here F k is the set of fnctions of protein k i i F F j j L1 L neighbors sho greatest oer-rep L3 neighbors sho little obserable oer-rep Keynote lectre for BioDM 006

10 Prediction Poer By Majority Voting Remoe oerlaps in leel-1 and leel- neighbors to stdy predictie poer of leel-1 only and leel- only neighbors Sensitiity s Precision analysis PR i = K i K k i m i S K i = K i k n i i Sensitiity Sensitiity s Precision L1 - L L - L1 L1 L Precision n i is no. of fn of protein i m i is no. of fn predicted for protein i k i is no. of fn predicted correctly for protein i leel- only neighbors performs better L1 L neighbors has greatest prediction poer Keynote lectre for BioDM 006

11 Fnctional Similarity Estimate: Czekanoski-Dice Distance Fnctional distance beteen to proteins (Brn et al 003) ( ) D k is the set of interacting partners of k X Δ Y is symmetric diff bet to sets X and Y Greater eight gien to similarity Similarity can be defined as S ( ) Keynote lectre for BioDM 006 = = 1 D( ) = Δ + X X + ( Y + Z) Is this a good measre if and hae ery diff nmber of neighbors?

12 Keynote lectre for BioDM 006 Fnctional Similarity Estimate: FS-Weighted Measre FS-eighted measre k is the set of interacting partners of k Greater eight gien to similarity Reriting this as ( ) S + + = ( ) Z X X Y X X S + + =

13 Correlation / Fnctional Similarity Correlation bet fnctional similarity & estimates Eqi measre slightly better in correlation / similarity for L1 & L neighbors Keynote lectre for BioDM 006

14 Reliability of Expt Sorces Diff Expt Sorces hae diff reliabilities Assign reliability to an interaction based on its expt sorces (abiea et al 004) Reliability bet and compted by: r = 1 (1 r) i E r i is reliability of expt sorce i E is the set of expt sorces in hich interaction bet and is obsered i Sorce Reliability Affinity Chromatography Affinity Precipitation Biochemical Assay Dosage Lethality 0.5 Prified Complex Reconstitted Complex 0.5 Synthetic Lethality Synthetic Resce 1 To Hybrid Keynote lectre for BioDM 006

15 Keynote lectre for BioDM 006 Fnctional Similarity Estimate: FS-Weighted Measre ith Reliability Take reliability into consideration hen compting FS-eighted measre: k is the set of interacting partners of k r is reliability eight of interaction bet and Reriting ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) = R r r r r r r r r r r r r r r S 1 1 ( ) Z X X Y X X S + + =

16 Integrating Reliability Eqi measre shos improed correlation / fnctional similarity hen reliability of interactions is considered: Keynote lectre for BioDM 006

17 Fnctional Similarity Estimate: Transitie FS Weighted Measre If protein is similar to and is similar to then proteins and may be similar also Transitie FS eighted measre S TR ( ) = max S ( ) max S ( ) S ( ) R R R Keynote lectre for BioDM 006

18 Integrating Transitiity Eqi measre shos improed correlation / fnctional similarity hen transitiity is considered: Keynote lectre for BioDM 006

19 Improement to Prediction Poer by Majority Voting Considering only neighbors / FS eight > 0. Keynote lectre for BioDM 006

20 Improement to Oer-Rep of Fnctions in eighbors Keynote lectre for BioDM 006

21 Use L1 & L eighbors for Prediction FS-eighted Aerage f x 1 ( ) λr π + S ( ) δ ( x) + S ( ) δ ( x) r int is fraction of all interaction pairs sharing fnction λ is eight of contribtion of backgrond freq δ(k x) = 1 if k has fnction x 0 otherise k is the set of interacting partners of k π x is freq of fnction x in the dataset Z is sm of all eights Keynote lectre for BioDM 006 = int x TR Z Z = 1+ S TR TR TR ( ) + S ( )

22 Performance of FS-Weighted Aeraging LOOCV comparison ith eighbor Conting Chi-Sqare PRODISTI Informatie FCs Sensitiity C Chi² PRODISTI Weighted Ag Precision Keynote lectre for BioDM 006

23 Performance of FS-Weighted Aeraging Dataset from Deng et al 003 Gene Ontology (GO) Annotations MIPS interaction dataset Comparison / eighbor Conting Chi-Sqare PRODISTI Marko Random Field FnctionalFlo Sensitiity Celllar Role C Chi² PRODISTI MRF FnctionalFlo Weighted Ag Precision Sensitiity Biochemical Fnction C Chi² PRODISTI MRF FnctionalFlo Weighted Ag Precision Sensitiity SbCelllar Location C Chi² PRODISTI MRF FnctionalFlo Weighted Ag Precision Keynote lectre for BioDM 006

24 Performance of FS-Weighted Aeraging Correct Predictions made on at least 1 fnction s mber of predictions made per protein 1 Correct Predictions s Predictions Made - Celllar Role 1 Correct Predictions s Predictions Made - SbCelllar Location Correct Predictictions s Predictions Made - Biochemical Fnction Fraction C Chi² PRODISTI FnctionalFlo Weighted Ag Predictions Fraction C Chi² PRODISTI FnctionalFlo Weighted Ag Predictions Fraction C Chi² PRODISTI FnctionalFlo Weighted Ag Predictions Keynote lectre for BioDM 006

25 Performance of FS-Weighted Aeraging Prediction performance frther improes after incorporation of interaction reliability Sensitiity Informatie FCs C Chi² PRODISTI Weighted Ag Weighted Ag R Precision Keynote lectre for BioDM 006

26 Incorporating Other Info Sorces PPI Interaction Data General Rep of Interaction Data Uniqe Pairs 4914 Proteins Reliability: (Based on fraction ith knon fnctional similarity) Seqence Similarity Smithaterman bet seq of all proteins For each seq among all SW scores / all other seq extract seq / SW score >= 3 standard deiations from mean 308 Uniqe Pairs 6766 Proteins Precision Precison s. Recall GRID Seq Similarity (SS) Expression GRID + SS All data sorces Reliability: Gene Expression Spellman / 77 timepoints Extract all pairs / Pearson s > Uniqe Pairs 08 Proteins Reliability: Keynote lectre for BioDM Correct Predictions

27 Conclsions Indirect fnctional association is plasible It is fond often in real interaction data It can be sed to improe protein fnction prediction from protein interaction data It shold be possible to incorporate interaction netorks extracted by literatre in the inference process ithin or frameork for good benefit Keynote lectre for BioDM 006

28 Hon ian Cha Wing Kin Sng Acknoledgements Keynote lectre for BioDM 006

29 References Breitkretz B. J. Stark C. and Tyers. (003) The GRID: The General Repository for Interaction Datasets. Genome Biology 4:R3 Brn C. Cheenet F. Martin D. Wojcik J. Genoche A. Jacq B. (003) Fnctional classification of proteins for the prediction of celllar fnction from a protein-protein interaction netork. Genome Biol. 5(1):R6 Deng M. Zhang K. Mehta S.Chen T. and Sn F. Z. (003) Prediction of protein fnction sing protein-protein interaction data. J. Comp. Biol. 10(6): Hishigaki H. akai K. Ono T. Tanigami A. and Takagi T. (001) Assessment of prediction accracy of protein fnction from proteinprotein interaction data Yeast 18(6): Lanckriet G. R. G. Deng M. Cristianini. Jordan M. I. and oble W. S. (004) Kernel-based data fsion and its application to protein fnction prediction in yeast. Proc. Pacific Symposim on Biocompting 004. pp Letosky S. and Kasif S. (003) Predicting protein fnction from protein/protein interaction data: a probabilistic approach. Bioinformatics. 19(Sppl.1):i197 i04 Keynote lectre for BioDM 006

30 References Repp A. Zollner A. Maier D. Albermann K. Hani J. Mokrejs M. Tetko I. Gldener U. Mannhapt G. Mnsterkotter M. Mees H.W. (004) The FnCat a fnctional annotation scheme for systematic classification of proteins from hole genomes. cleic Acids Res. 14:3(18): Samanta M. P. Liang S. (003) Predicting protein fnctions from redndancies in large-scale protein interaction netorks. Proc atl. Acad. Sci. U S A. 100(): Schikoski B. Uetz P. and Fields S. (000) A netork of interacting proteins in yeast. atre Biotechnology 18(1): Titz B. Schlesner M. and Uetz P. (004) What do e learn from highthroghpt protein interaction data? Expert Re.Proteomics 1(1): Vazqez A. Flammi A. Maritan A. and Vespignani A. (003) Global protein fnction prediction from protein-protein interaction netorks. atre Biotechnology. 1(6): Zho X. Kao M. C. Wong W. H. (00) Transitie fnctional annotation by shortest-path analysis of gene expression data. Proc. atl. Acad. Sci. U S A. 99(0): Keynote lectre for BioDM 006