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MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON

MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics? How can we use this information to improve human health and quality of life?. PROTEOME. ~60,000 in human. ~60,000 in rice.

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MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON

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  1. MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics? How can we use this information to improve human health and quality of life?

  2. PROTEOME ~60,000 in human ~60,000 in rice ~4500 in bacteria Several thousand distinct sequence families

  3. STRUCTURE A few thousand distinct structural folds

  4. FUNCTION Tens of thousands of functions

  5. EXPRESSION Different expression patterns based on time and location

  6. INTERACTION Interaction and expression are interdependent with structure and function

  7. PROTEIN FOLDING Protein sequence …-L-K-E-G-V-S-K-D-… One amino acid Unfolded protein Spontaneous self-organisation (~1 second) Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… • Not unique • Mobile • Inactive • Expanded • Irregular

  8. PROTEIN FOLDING 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 • Not unique • Mobile • Inactive • Expanded • Irregular • Unique shape • Precisely ordered • Stable/functional • Globular/compact • Helices and sheets

  9. STRUCTURE One distance constraint for every six residues One distance constraint for every ten residues 0 2 4 6 ACCURACY Experiment (X-ray, NMR) Computation (de novo) Computation (template-based) Hybrid (Iterative Bayesian interpretation of noisy NMR data with structure simulations) Cα RMSD

  10. STRUCTURE T0290 – peptidyl-prolyl isomerase from H. sapiens T0288 – PRKCA-binding from H. sapiens 2.2 Å Cα RMSD for 93 residues (25% identity) 0.5 Å Cα RMSD for 173 residues (60% identity) T0332 – methyltransferase from H. sapiens T0364 – hypothetical from P. putida 2.0 Å Cα RMSD for 159 residues (23% identity) 5.3 Å Cα RMSD for 153 residues (11% identity) Liu/Hong-Hung/Ngan

  11. FUNCTION Ion binding energy prediction with a correlation of 0.7 Calcium ions predicted to < 0.05 Å RMSD in 130 cases Meta-functional signature accuracy Meta-functional signature for DXS model from M. tuberculosis Wang/Cheng

  12. INTERACTION Transcription factor bound to DNA promoter regulog model from S. cerevisiae Prediction of binding energies of HIV protease mutants and inhibitors using docking with dynamics BtubA/BtubBinterolog model from P. dejongeii (35% identity to eukaryotic tubulins) McDermott/Wichadakul/Staley/Horst/Manocheewa/Jenwitheesuk/Bernard

  13. SYSTEMS Example predicted protein interaction network from M. tuberculosis (107 proteins with 762 unique interactions) Proteins PPIs TRIs H. sapiens 26,741 17,652 828,807 1,045,622 S. cerevisiae 5,801 5,175 192,505 2,456 O.sativa (6) 125,568 19,810 338,783 439,990 E. coli 4,208 885 1,980 54,619 In sum, we can predict functions for more than 50% of a proteome, approximately ten million protein-protein and protein-DNA interactions with an expected accuracy of 50%. Utility in identifying function, essential proteins, and host pathogen interactions McDermott/Wichadakul

  14. SYSTEMS Combining protein-protein and protein-DNA interaction networks to determine regulatory circuits McDermott/Rashid/Wichadakul

  15. INFRASTRUCTURE ~500,000 molecules over 50+proteomes served using a 1.2 TB PostgreSQL database and a sophisticated AJAX webapplication and XML-RPC API http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu Guerquin/Frazier

  16. INFRASTRUCTURE Guerquin/Frazier

  17. INFRASTRUCTURE http://bioverse.compbio.washington.edu/integrator Chang/Rashid

  18. APPLICATION: RICE INTERACTOMICS Proteome Number Number Number Number of annotated in of proteins (%) protein protein network interactions O. sativa japonica KOME cDNAs 25,875 11,841 (44%) 4705 88,102 O. sativa indica BGI 9311 40,925 22,278 (55%) 5849 95,149 O. sativa japonica Syngenta 38,071 20,874 (55%) 5911 104,640 O. sativa indica IRGSP 36,658 20,481 (56%) 5835 110,118 O. sativa japonica nrKOME cDNAs 19,057 7478 (39%) 3047 38,793 O. sativa indica BGI pa64 37,712 15,286 (41%) 5780 98,779 Total 198,298 98,238 (50%) 31,127 535,581Total (unique) 125,568 60,272 (48%) 19,810 338,783 http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu McDermott/Wichadakul

  19. APPLICATION: RICE INTERACTOMICS BGI/McDermott

  20. APPLICATION: DRUG DISCOVERY CMV KHSV HSV Jenwitheesuk

  21. APPLICATION: DRUG DISCOVERY Computionally predicted broad spectrum human herpesvirus protease inhibitors is effective in vitro against members from all three classes and is comparable or better than anti-herpes drugs CMV HSV KHSV Our protease inhibitor acts synergistically with acylovir (a nucleoside analogue that inhibits replication) and it is less likely to lead to resistant strains compared to acylovir HSV HSV Lagunoff

  22. APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

  23. FUTURE + + Computational biology Structural genomics Functional genomics MODELLING PROTEIN AND PROTEOME STRUCTURE FUNCTION AT THE ATOMIC LEVEL IS NECESSARY TO UNDERSTAND THE RELATIONSHIPS BETWEEN SINGLE MOLECULES, SYSTEMS, PATHWAYS, CELLS, AND ORGANISMS

  24. ACKNOWLEDGEMENTS Current group members: Past group members: • Baishali Chanda • Brady Bernard • Chuck Mader • Ersin Emre Oren • Ekachai Jenwitheesuk • Gong Cheng • Imran Rashid • Jeremy Horst • Ling-Hong Hung • Michal Guerquin • Shu Feng • Siriphan Manocheewa • Somsak Phattarasukol • Stewart Moughon • Tianyun Liu • Vania Wang • Weerayuth Kittichotirat • Zach Frazier • Reene Ireton, Program Manager • Aaron Chang • David Nickle • Duangdao Wichadukul • Duncan Milburn • Jason McDermott • Marissa LaMadrid • Kai Wang • Kristina Montgomery • Shing-Chung Ngan • Vanessa Steinhilb • Yi-Ling Cheng

  25. ACKNOWLEDGEMENTS Collaborators: • BGI • Gane Wong • Jun Yu • Jun Wang • et al. • BIOTEC/KMUTT • MSE • Mehmet Sarikaya • Candan Tamerler • et al. • UW Microbiology • James Staley • John Mittler • Michael Lagunoff • Roger Bumgarner • Wesley Van Voorhis • et al. Funding agencies: • National Institutes of Health • National Science Foundation • -DBI • -IIS • Searle Scholars Program • Puget Sound Partners in Global Health • Washington Research Foundation • UW • -Advanced Technology Initiative • -TGIF

  26. E. coli INTERACTIONS McDermott

  27. M. tuberculosisINTERACTIONS McDermott

  28. C. elegans INTERACTIONS McDermott

  29. H. sapiens INTERACTIONS McDermott

  30. Network-based annotation for C. elegans McDermott

  31. KEY PROTEINS IN ANTHRAX Articulation points McDermott

  32. HOST PATHOGEN INTERACTIONS McDermott

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