Modelling proteomes Ram Samudrala Department of Microbiology

1. Modelling proteomes Ram Samudrala Department of Microbiology 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. Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein

8. Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) unique shape precisely ordered stable/functional globular/compact helices and sheets Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein

9. Methods for obtaining structure Experimental Theoretical X-ray crystallography NMR spectroscopy De novo prediction Homology modelling

10. De novo prediction of protein structure sample conformational space such that native-like conformations are found select hard to design functions that are not fooled by non-native conformations (“decoys”) astronomically large number of conformations 5 states/100 residues = 5100 = 1070

11. Semi-exhaustive segment-based folding Make random moves to optimise what is observed in known structures generate … … Find the most protein-like structures minimise … … all-atom pairwise interactions, bad contacts compactness, secondary structure, consensus of generated conformations filter EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK

12. Critical Assessment of protein Structure Prediction methods (CASP) Pre-CASP CASP Bias towards known structures Blind prediction

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

14. CASP6 prediction (model1) for T0281 4.3 Å Cα RMSD for all 70 residues Ling-Hong Hung/Shing-Chung Ngan

15. Homologous proteins share similar structures Gan et al, Biophysical Journal 83: 2781-2791, 2002

16. Comparative modelling of protein structure scan align KDHPFGFAVPTKNPDGTMNLMNWECAIP KDPPAGIGAPQDN----QNIMLWNAVIP ** * * * * * * * ** … … build initial model construct non-conserved side chains and main chains minimum perturbation graph theory, semfold refine physical functions de novo simulation

17. CASP6 prediction (model1) for T0231 1.3 Å Cα RMSD for all 137 residues (80% ID) Tianyun Liu

18. CASP6 prediction (model1) for T0271 2.4 Å Cα RMSD for all 142 residues (46% ID) Tianyun Liu

19. Similar global sequence or structure does not imply similar function TIM barrel proteins 2246 with known structure hydrolase ligase lyase oxidoreductase transferase

20. Qualitative function classification sequence-based structure-based Kai Wang

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

22. Prediction of protein interaction networks Target proteome Interacting protein database 85% protein a protein A experimentally determined interaction predicted interaction protein B 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 Jason McDermott

23. E. coli predicted protein interaction network Jason McDermott

24. M. tuberculosis predicted protein interaction network Jason McDermott

25. C. elegans predicted protein interaction network Jason McDermott

26. H. sapiens predicted protein interaction network Jason McDermott

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

28. Identifying key proteins on the anthrax predicted network Articulation point proteins Jason McDermott

29. Identification of virulence factors Jason McDermott

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

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

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

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

34. Bioverse - Integrator Aaron Chang

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

36. 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