1. Modelling proteomes An integrated computational framework for systems biology research Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

2. Proteome – all proteins of a particular system ~60,000 in human ~60,000 in rice ~4500 in bacteria like Salmonella and E. coli Several thousand distinct sequence families

3. Modelling proteomes – understand the structure of individual proteins A few thousand distinct structural folds

4. Modelling proteomes – understand their individual functions Thousands of possible functions

5. Modelling proteomes – understand their expression Different expression patterns based on time and location

6. Modelling proteomes – understand their interactions Interactions and expression patterns are interdependent with structure and function

7. CASP6 prediction (model1) for T0215 5.0 Å C α RMSD for all 53 residues Ling-Hong Hung/Shing-Chung Ngan

8. CASP6 prediction (model1) for T0281 4.3 Å C α RMSD for all 70 residues

9. Tianyun Liu CASP6 prediction (model1) for T0231 1.3 Å C α RMSD for all 137 residues (80% ID)

10. CASP6 prediction (model1) for T0271 2.4 Å C α RMSD for all 142 residues (46% ID) Tianyun Liu

11. Prediction of HIV-1 protease-inhibitor binding energies with MD Ekachai Jenwitheesuk Can predict resistance/susceptibility to six FDA approved inhibitors with 95% accuracy in conjunction with knowledge-based methods http://protinfo.compbio.washington.edu/pirspred/

12. Prediction of protein interaction networks Jason McDermott Interacting protein database protein a protein b experimentally determined interaction Target proteome protein A 85% predicted interaction protein B 90% Assign confidence based on similarity and strength of interaction Key paradigm is the use of homology to transfer information across organisms; not limited to yeast, fly, and worm Consensus of interactions helps with confidence assignments

13. E. coli predicted protein interaction network Jason McDermott

14. M. tuberculosis predicted protein interaction network Jason McDermott

15. C. elegans predicted protein interaction network Jason McDermott

16. H. sapiens predicted protein interaction network Jason McDermott

17. Network-based annotation for C. elegans Jason McDermott

18. Articulation point proteins Identifying key proteins on the anthrax predicted network

19. Jason McDermott Identification of virulence factors

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

21. Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

22. Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

23. Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

24. Bioverse - Integrator Aaron Chang

25. Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution Where is all this going? Structural genomics Functional genomics + Computational biology +

26. Acknowledgements Aaron Chang Chuck Mader David Nickle Ekachai Jenwitheesuk Gong Cheng Jason McDermott Kai Wang Ling-Hong Hung Mike Inouye Michal Guerquin Stewart Moughon Shing-Chung Ngan Tianyun Liu Zach Frazier National Institutes of Health National Science Foundation Searle Scholars Program (Kinship Foundation) UW Advanced Technology Initiative in Infectious Diseases http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu