1. Modelling proteomes Ram Samudrala University of Washington

2. What is a “proteome”? All proteins of a particular system (organelle, cell, organism) What does it mean to “model a proteome”? For any protein, we wish to: - figure out what it looks like (structure or form) - understand what it does (function) Repeat for all proteins in a system Understand the relationships between all of them ANNOTATION { EXPRESSION + INTERACTION }

3. Figuring out protein structure Experimental methods: - X-ray crystallography, NMR, electron diffraction - Accurate but slow Predictive methods: - De novo prediction, comparative modelling - Automated and relatively fast; not as accurate experimental methods

4. CASP5 prediction for T138 4.6 Å Cα RMSD for 84 residues

5. CASP5 prediction for T146 5.6 Å Cα RMSD for 67 residues

6. CASP5 prediction for T170 4.8 Å Cα RMSD for all 69 residues

7. CASP5 prediction for T129 5.8 Å Cα RMSD for 68 residues

8. CASP5 prediction for T172 5.9 Å Cα RMSD for 74 residues

9. CASP5 prediction for T187 5.1 Å Cα RMSD for 66 residues

10. CASP5 prediction for T129 1.0 Å Cα RMSD for 133 residues (57% id)

11. CASP5 prediction for T182 1.0 Å Cα RMSD for 249 residues (41% id)

12. CASP5 prediction for T150 2.7 Å Cα RMSD for 99 residues (32% id)

13. CASP5 prediction for T185 6.0 Å Cα RMSD for 428 residues (24% id)

14. CASP5 prediction for T160 2.5 Å Cα RMSD for 125 residues (22% id)

15. CASP5 prediction for T133 6.0 Å Cα RMSD for 260 residues (14% id)

16. Figuring out protein function Experimental methods: - A variety of single molecule and genomic/proteomic techniques - Usually not complete; slow Predictive methods: - Structure and sequence based methods using experimental knowledge of known functions - Automated and relatively fast; not as accurate as experimental methods; may be comprehensive

17. Structure and sequence based functional studies Using structure to rationalise putative function: - Bacterial tubulin study with Jim Staley (PNAS 2003) Using structure to rationalise mutagenesis data: - Dr adhesin study with Steve Moseley (Molec. Micro. 2002) - Invb study with Sam Miller Using structure and sequence to predict inhibitor binding: - Protease inhibitor binding studies using docking (BMC 2003) - Sequence-based prediction using regression models for HIV protease and RT with John Mittler (submitted) Structure and sequence based methods for functional classification: - In progress - General method to assign function from sequence - Analyse binding of 130K compounds to structures - Build machine learning models of functional families

18. Prediction of SARS CoV proteinase inhibitors Ekachai Jenwitheesuk

19. Integrated structural and functional annotation of proteomes structure based methods microenvironment analysis zinc binding site? structure comparison homology function? sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses + * * * * Bioverse * * assign function to entire protein space experimental data single molecule + genomic/proteomic + EXPRESSION + INTERACTION }

20. Bioverse – explore relationships among molecules and systems Jason McDermott http://bioverse.compbio.washington.edu

21. Bioverse – explore relationships among molecules and systems Jason Mcdermott

22. Bioverse – prediction of protein interaction networks Jason Mcdermott Interacting protein database protein α protein β experimentally determined interaction Target proteome protein A 85% predicted interaction protein B 90% Assign confidence based on similarity and strength of interaction

23. Bioverse – E. coli predicted protein interaction network Jason McDermott

24. Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott

25. Bioverse – C. elegans predicted protein interaction network Jason McDermott

26. Bioverse – H. sapiens predicted protein interaction network Jason McDermott

27. Example uses of the Bioverse annotations and predicted networks Network-based corroboration of functional annotation: - Proteins that interact with each other have related or identical functions - Highly accurate Identification of essential proteins: - analyse key positions (central or articulation points) in the networks - 30-40% accuracy; useful for hypothesis validation Identification of virulence factors and molecular mimicry: - analyse host-pathogen protein interaction networks - analyse host-pathogen protein similarity networks Evolutionary analysis and genetic engineering: - compare networks from different organisms/strains - mix and match proteins and sub-networks

28. Bioverse – network-based annotation for C. elegans Jason McDermott

29. Articulation point proteins Bioverse – identifying key proteins on the anthrax predicted network

30. Jason McDermottDefense-related proteins Bioverse – identifying key proteins on the rice predicted network

31. Bioverse – viewer Aaron Chang

32. Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution

33. Acknowledgements Aaron Chang Ashley Lam Ekachai Jenwitheesuk Gong Cheng Jason McDermott Kai Wang Ling-Hong Hung Lynne Townsend Marissa LaMadrid Mike Inouye Stewart Moughon Shing-Chung Ngan Yi-Ling Cheng Zach Frazier National Institutes of Health National Science Foundation Searle Scholars Program (Kinship Foundation) UW Advanced Technology Initative in Infectious Diseases