1. Predicting PDZ domain protein- protein interactions from the genome Gary Bader Donnelly Centre for Cellular and Biomolecular Research University of Toronto VanBUG, Vancouver, Jan.8.2009 http://baderlab.org

2. Computational Cell Map Cary MP et al. Pathway information… FEBS Lett. 2005 Bader GD et al. Functional genomics and proteomicsTrends Cell Biol. 2003 Map the cell Predict map from genome Multiple perturbation mapping Active cell map Map visualization and analysis software Read map to understand Cell processes Gene function Disease effects Map evolution

3. How are biological networks in the cell encoded in the genome? Can we accurately predict biologically relevant interactions from a genome? How do genome sequence changes underlying disease affect the molecular network in the cell? Can we predict how well model pathways or phenotypes will translate to human? Can we design new networks de novo?

4. Predicting Protein Interaction Networks From the Genome Ideally: Reality: –Not currently possible –Signaling pathways too divergent to accurately map by orthology –Protein interaction prediction likely as hard as protein folding, in general e.g. induced fit Accurately Predict

5. Map via orthology relationships –Metabolic pathways E.g. KEGG, BioCyc, metaSHARK –Protein-protein interactions E.g. OPHID, HomoMINT –Signaling pathways E.g. Reactome Infer using functional associations –Phylogenetic profile, Rosetta Stone Infer from molecular profiles –Gene expression  gene regulatory network –E.g. ARACNE, MEDUSA, MatrixREDUCE Predicting Networks Bader & Enright Pinney et al. NAR 2005

6. Peptide Recognition Domains Simple binding sites Well studied Numerous Biologically important –Eukaryotic signaling systems often involve modular protein- protein interaction domains http://pawsonlab.mshri.on.ca/ http://nashlab.uchicago.edu/domains/

7. Protein Domain Interaction Network Prediction Genome Gene and protein prediction Domain prediction Specificity prediction Protein-protein interaction prediction

8. Protein Domain Interaction Network Prediction Genome Gene and protein prediction Domain prediction Specificity prediction Protein-protein interaction prediction

9. Par-6 PDZ Domain VKESLV-COOH (1RZX, Fly) PDZ Domains 80-90 aa’s, 5-6 beta strands, 2 alpha helices Recognize hydrophobic C-termini Membrane localization of signaling components Neuronal development, cell polarity, ion channel regulation C Tonikian et al. PLoS Biology Sep.2008 Dev Sidhu

10. ~250 Human PDZ Domains Multiple sequence alignment

11. ~250 Human PDZ Domains Multiple sequence alignment

12. C-Terminus PDZ Binding Motifs polar basic acidic hydrophobic Class 1: X[T/S]X  Class 2: X  X 

13. Sequence Logo SWWPDSWV NAFEETWV NPFWDVWV SVDVDTWV -AYFDTWV STFLETWV KGVFESWV ESWHDSWV -GDQDTWV GRWMDTWV KFWRDTWL … Profile polar=green, basic=blue, acidic=red, hydrophobic=black Logo Position Amino Acid http://weblogo.berkeley.edu/ Schneider TD, Stephens RM. 1990. Nucleic Acids Res. 18:6097-6100

14. 82 worm and human PDZ specificities mapped by phage display ~3100 peptides

15. PDZ Specificity Map Class 1: X[T/S]X  Class 2: X  X 

16. PDZ Specificity Map Class 1: X[T/S]X  Class 4: XGX  Class 3: X[D/X]X  Class 2: X  X 

17. Class 1: X[T/S]X  Class 4: XGX  Class 3: X[D/X]X  Class 2: X  X  PDZ Specificity Map 16 Classes

18. Specificity at Most Positions

19. Position Versatile Many Distinct Specificities

20. Versatile and Robust 91 Erbin mutants phaged, 3400 peptides Mutations cause specificity switch, not function loss

21. Conserved Specificity, Expanded Use PDZ domains are versatile, but only ~16 classes used from worm to human One billion years of evolution Model: specificities arose early, domains expanded under evolutionary constraints Raffi Tonikian

22. Protein Domain Interaction Network Prediction Genome Gene and protein prediction Domain prediction Specificity prediction Protein-protein interaction prediction

23. Predicting PDZ Specificity >ERBB2IP-1 RVRVEKDPELGFSISGGVGGRGNPFRPDDDGIFVTRVQPE GPASKLLQPGDKIIQANGYSFINIEHGQAVSLLKTFQNTVELII Tonikian et al. PDZ specificity map

24. Sequence Predicts Specificity

25. 50 mapped PDZ domains >70% similar to 69 unmapped PDZ Double coverage to 45% of worm/human 33 more PDZ groups 110 singletons Mapped UnmappedWorm Human

26. Are Residues Correlated? ~80 ~3000 Boris Reva, Chris Sander

27. Domain Position Peptide Position Joint Freq Domain Freq Peptide Freq Mutual Information (H@105)(T@7)88613679130.166384111 (P@53)(T@7)3734119130.130328629 (Q@67)(W@8)36637710370.117349366 (V@109)(T@7)83614309130.115598151 (S@64)(E@6)2183864140.109298916 (V@9)(W@4)1502023400.109096478 (A@102)(E@6)2284294140.107661006 (L@30)(S@6)2073833840.106889284 (P@53)(E@6)2194114140.103683514 (L@26)(E@6)39111384140.10274842 Top 10 1-1 Rules

28. Correlation Validation

29. Prediction Can Be Accurate Experiment Prediction

30. Challenge: But Not Always Experiment Prediction Shirley Hui

31. Predicting PDZ Specificity Machine Learning Predictions YES NO … Negative: Positive: … YES NO Test Examples (PDZ-Peptide Pairs) ???? … Training Examples (Binding and Non binding PDZ-Peptide Pairs) … Shirley Hui, Xiaojian Shao Consider sequence and physicochemical properties high accuracy at matching known domains to peptides

32. Protein Domain Interaction Network Prediction Genome Gene and protein prediction Domain prediction Specificity prediction Protein-protein interaction prediction

33. Genome Search SWWPDSWV NAFEETWV NPFWDVWV SVDVDTWV -AYFDTWV STFLETWV KGVFESWV ESWHDSWV -GDQDTWV GRWMDTWV KFWRDTWL … Profile Phage Results polar=green, basic=blue, acidic=red, hydrophobic=black ERBIN PDZ

34. >Q86W91_HUMAN Plakophilin 4, isoform b...LKSTTNYVDFYSTKRPSYRAEQYPGSPDSWV QYPGSPDSWV Genome Search DSWV PDZ ERBIN C-Terminal Match Score 5.5 Predicted C-Terminal Motif Assumes: Position independence, uniform input, good sampling Physiological binder is similar to phage sequence

35. Known Interactor High Score Prediction Can be Accurate ERBIN PDZ Interaction Prediction 10E-5 (High) Probability of PDZ binding 10E-7 (Low) ERBB2IP-1 …but requires further experimental support

36. ...

37. p-value Network of prioritized human PDZ interactions 336 interactions between 54 PDZ domains, 247 proteins Matches known biology, significantly enriched in known interactors 8% overlap, p=8.6x10 -18

38. In vitro Biologically Relevant (In vivo) Future: In vivo Protein Interaction Prediction PDZ Phage Display Genome Protein Expression Protein Function Protein Location Protein Structure Evolutionary Context Network Context Bind DLGsNMDAR In silico Predictions Peptides

39. PDZ Human-Virus Interactions 89 viral proteins matched better than any human protein (vs. 30 domains) Affinities (ELISA) Yingnan Zhang

40. Crtam peptide inhibitor blocks SCRIB-3 binding and polarization Synthetic viral peptide promotes T cell proliferation T cell Jung-Hua Yeh and Andrew Chan Crtam Ig transmembrane protein important in late phase T cell activation Non SCRIB bindingSCRIB Binding Non SCRIB binding SCRIB Binding

41. Conclusions PDZ domains are highly specific, versatile and robust to mutation Many specificities possible, but only a few are used Specificity can be predicted from domain sequence Prioritize predictions for experimental follow up Use by pathogens PDZ specificity map useful for: –Novel protein interaction discovery –Peptidomimetic therapeutic design –PDZ design (synthetic biology)

42. Cell map exploration and analysis Databases Literature Expert knowledge Experimental Data Can we accurately predict protein interactions? Pathway Information Pathway Analysis (Cytoscape)

43. http://pathguide.org Vuk Pavlovic ~280 Pathway Databases!

44. Pathway Commons: A Public Library Books: Pathways Lingua Franca: BioPAX OWL Index: cPath pathway database software Translators: translators to BioPAX Open access, free software No competition: Author attribution Aggregate ~ 20 databases in BioPAX format http:pathwaycommons.org Sander Lab (MSKCC) Bader Lab

45. Network visualization and analysis UCSD, ISB, Agilent, MSKCC, Pasteur, UCSF, Unilever, U of Toronto, U of Michigan http://cytoscape.org Pathway comparison Literature mining Gene Ontology analysis Active modules Complex detection Network motif search

46. Gene Function Prediction Guilt-by-association principle Biological networks are combined intelligently to optimize prediction accuracy Algorithm is more fast and accurate than its peers http://www.genemania.org Quaid Morris (CCBR) Rashad Badrawi, Ovi Comes, Sylva Donaldson, Christian Lopes, Jason Montojo, Khalid Zuberi

47. Canadian Bioinformatics Workshops 2009 Interpreting Gene Lists from -omics Studies Date: July 9-10, 2009, Toronto Faculty: Gary Bader, Quaid Morris & Wyeth Wasserman Clinical Genomics and Biomarker Discovery Date: July 16-17, 2009, Toronto Faculty: Sohrab Shah Informatics on High-Throughput Sequencing Data Date: July 23-24, 2009, Toronto Faculty: Michael Brudno, Asim Siddiqui & Francis Ouellette Exploratory Data Analysis and Essential Statistics using R October 2-3, 2009, Toronto Faculty: Raphael Gottardo and Boris Steipe Applications now being accepted at www.bioinformatics.ca Limited registration Registration Fee: $500

48. Acknowledgements PDZ Work Genentech Dev Sidhu Yingnan Zhang Heike Held Stephen Sazinsky Yan Wu University of Toronto Charlie Boone Raffi Tonikian, Xiaofeng Xin MSKCC Chris Sander Boris Reva Cytoscape Trey Ideker (UCSD) Kei Ono, Mike Smoot, Peng Liang Wang (Ryan Kelley, Nerius Landys, Chris Workman, Mark Anderson, Nada Amin, Owen Ozier, Jonathan Wang) Lee Hood (ISB) Sarah Killcoyne, John Boyle, Ilya Shmulevich (Iliana Avila-Campillo, Rowan Christmas, Andrew Markiel, Larissa Kamenkovich, Paul Shannon) Benno Schwikowski (Pasteur) Mathieu Michaud (Melissa Cline, Tero Aittokallio) Chris Sander (MSKCC) Ethan Cerami, Ben Gross (Robert Sheridan) Annette Adler (Agilent) Allan Kuchinsky, Mike Creech (Aditya Vailaya) Bruce Conklin (UCSF) Alex Pico, Kristina Hanspers Bader Lab G2N Chris Tan David Gfeller Shirley Hui Xioajian Shao Shobhit Jain MP Anastasija Baryshnikova Iain Wallace Laetitia Morrison Ron Ammar ACM Daniele Merico Ruth Isserlin Vuk Pavlovic Oliver Stueker Pathway Commons Chris Sander Ethan Cerami Ben Gross Emek Demir Robert Hoffmann Igor Rodchenkov Rashad Badrawi Funding CIHR, NSERC, NIH Genome Canada Canada Foundation for Innovation/ORF http://baderlab.org