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 state DNA …-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-… 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 state DNA …-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-… protein sequence …-L-K-E-G-V-S-K-D-… one amino acid unfolded protein

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

10. Semi-exhaustive segment-based folding continuous f,y distributions local and global moves generate … … monte carlo with simulated annealing conformational space annealing, GA minimise … … all-atom pairwise interactions, bad contacts compactness, secondary structure, density of generated conformations filter EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK

11. CASP6 prediction for T0215 Model 1 2.52 Å 5.06 Å Ling-Hong Hung/Shing-Chung Ngan

12. CASP6 prediction for T0236 Model 5 3.63 Å 5.42 Å Ling-Hong Hung/Shing-Chung Ngan

13. CASP6 prediction for T0281 Model 1 2.25 Å 4.31 Å Ling-Hong Hung/Shing-Chung Ngan

14. 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

15. CASP6 prediction for T0247 Model 1 Tianyun Liu

16. CASP6 prediction for T0271 Parent 1 Parent 3 Parent2 Tianyun Liu Model 1

17. CASP6 overall summaries Tianyun Liu

18. Similar global sequence or structure does not imply similar function

19. Qualitative function classification Kai Wang

20. 1.0 0.5 with MD without MD Correlation coefficient ps 0 0.2 0.4 0.6 0.8 1.0 MD simulation time Prediction of HIV-1 protease-inhibitor binding energies with MD Ekachai Jenwitheesuk

21. Prediction of inhibitor resistance/susceptibility http://protinfo.compbio.washington.edu/pirspred/ Kai Wang / Ekachai Jenwitheesuk

22. Integrated structural and functional annotation of proteomes structure based methods microenvironment analysis structure comparison sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses zinc binding site? homology function? + Assign function to entire protein space: key paradigm is use of homology to transfer information across organisms * Bioverse * * } * EXPRESSION + INTERACTION * * + experimental data single molecule + genomic/proteomic

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

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

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

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

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

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

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

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

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

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

33. Bioverse – identifying key proteins on the anthrax predicted network Articulation point proteins Jason McDermott

34. Bioverse – identification of virulence factors Jason McDermott

35. Bioverse - Integrator Aaron Chang

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

37. 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