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 …-L-K-E-G-V-S-K-D-… …-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-… one amino acid DNA protein sequence unfolded protein native state spontaneous self-organisation (~1 second) not unique mobile inactive expanded irregular

8. Protein folding …-L-K-E-G-V-S-K-D-… …-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-… one amino acid DNA protein sequence unfolded protein native state spontaneous self-organisation (~1 second) unique shape precisely ordered stable/functional globular/compact helices and sheets not unique mobile inactive expanded irregular

9. De novo prediction of protein structure sample conformational space such that native-like conformations are found astronomically large number of conformations 5 states/100 residues = 5 100 = 10 70 select hard to design functions that are not fooled by non-native conformations (“decoys”)

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

11. 2.52 Å5.06 Å Model 1 CASP6 prediction for T0215 Ling-Hong Hung/Shing-Chung Ngan

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

13. 2.25 Å4.31 Å Model 1 CASP6 prediction for T0281 Ling-Hong Hung/Shing-Chung Ngan

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

15. T0247RAPDFTMscoreRMSDMaxSub cf-model-30.140.84484.0550.6563 parent 1-27.090.83914.1080.6446 parent 2-26.680.83184.1940.625 parent 3-26.590.82524.1970.6051 parent 4-26.250.8393.9810.6281 parent 5-18.510.84223.9790.6416 CASP6 prediction for T0247 Model 1 Tianyun Liu

16. Model 1 Parent 1 Parent 2 Parent 3 T0247RAPDFTM-scoreRMSDMaxSub cf-model-37.440.87182.1660.7911 parent 1-34.870.86622.2330.7789 parent 2-33.990.82482.1660.7402 parent 3-36.830.82542.1390.7456 CASP6 prediction for T0271 Tianyun Liu

17. CASP6 overall summaries Tianyun Liu

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

19. Qualitative function classification Kai Wang

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

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

22. 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: key paradigm is use of homology to transfer information across organisms experimental data single molecule + genomic/proteomic + EXPRESSION + INTERACTION }

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

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

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

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

27. 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

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

34. Jason McDermott Bioverse – identification of virulence factors

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