1. Modelling proteins and proteomes using Linux clusters Ram Samudrala University of Washington

2. Examples of biological problems Protein structure prediction/docking simulations - need to run different trajectories that sometimes talk with each other Molecular dynamics simulations - need more cohesive parallelisation Polarisable force fields - need true parallelisation Bioinformatics searches/exploration - trivially parallelisable

3. Computational issues Need efficient methods to start/stop jobs Need load/balancing queuing system Need fast communications at times Need stability (months/years uptimes) Need low maintainance/management overhead Need low installation overhead Needs to be cheap!

4. Hardware and operating system 256 AMD and Intel CPUs (1-2.5 GHz) 0.5-1 GB RAM, 100-200 GB HD, dual processor MBs 100Mbps ethernet connectivity for 64 processor sets White boxes are good but use up space – 1u racks ideal Minimal Linux installation – create clone “CD” – copy on all machines

5. Our solution No single solution – user implements their own Completely decentralised Analyse problem and determine parallelisable parts Implementation specific to problem Use local scratch space for computation Redundant storage of data for faster access Limit problem space to specific problems

6. Problem specific implementation MCSA/GA: socket-based communication of trajectories; multiple trajectories on different CPUs Docking: sample different ligands/regions of the protein on different CPUs MD: Pairwise force-fields are additive PFF: ? Bioinformatics: trivial parallelisation; communication by disk

7. Modelling proteomes Ram Samudrala University of Washington

8. 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 }

9. 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

10. 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

11. 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”)

12. Semi-exhaustive segment-based folding EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK generate fragments from database 14-state ,  model …… minimise monte carlo with simulated annealing conformational space annealing, GA …… filter all-atom pairwise interactions, bad contacts compactness, secondary structure

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

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

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

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

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

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

19. 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

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

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

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

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

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

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

26. Prediction of SARS CoV proteinase inhibitors Ekachai Jenwitheesuk

27. Computational aspects of structural genomics D. ab initio prediction C. fold recognition * * * * * * * * * * B. comparative modelling A. sequence space * * * * * * * * * * * * E. target selection targets F. analysis * * (Figure idea by Steve Brenner.)

28. Computational aspects of functional genomics structure based methods microenvironment analysis zinc binding site? structure comparison homology function? sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses + experimental data single molecule + genomic/proteomic + * * * * Bioverse * * assign function to entire protein space

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

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

31. 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

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

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

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

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

36. Bioverse – organisation of the interaction networks Jason McDermott C i = 2n/k i (k i -1)

37. Jason McDermottDefense-related proteins Bioverse – mapping pathways on the rice predicted network

38. Jason McDermottTryptophan biosynthesis

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

40. Bioverse – H. sapiens protein-protein similarity network

41. Bioverse – viewer Aaron Chang

42. Future directions Network connection with multiple ethernet cards based on traffic analysis Gigabit ethernet (switches are still expensive) Better network filesystems

43. Take home message Prediction of protein structure and function can be used to model whole genomes to understand organismal function and evolution

44. 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