1. MODELLING PROTEOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics?

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

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

4. INTERACTION McDermott/Wichadakul/Staley/Horst/Manocheewa/Jenwitheesuk/Bernard BtubA/BtubB interolog model from P. dejongeii (35% identity to eukaryotic tubulins) 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

5. SYSTEMS McDermott/Wichadakul Example predicted protein interaction network from M. tuberculosis (107 proteins with 762 unique interactions) 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 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

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

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

8. APPLICATION: DRUG DISCOVERY HSV KHSV CMV 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 HSV 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 Lagunoff

9. APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

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

11. ACKNOWLEDGEMENTS Baishali Chanda Brady Bernard Chuck Mader David Nickle Ersin Emre Oren Ekachai Jenwitheesuk Gong Cheng Imran Rashid Jeremy Horst Ling-Hong Hung Michal Guerquin Rob Brasier Rosalia Tungaraza Shing-Chung Ngan Siriphan Manocheewa Somsak Phattarasukol Stewart Moughon Tianyun Liu Vania Wang Weerayuth Kittichotirat Zach Frazier Kristina Montgomery, Program Manager Current group members: Aaron Chang Duncan Milburn Jason McDermott Kai Wang Marissa LaMadrid Past group members: Funding agencies: National Institutes of Health National Science Foundation Searle Scholars Program Puget Sound Partners in Global Health UW Advanced Technology Initiative Washington Research Foundation UW TGIF James Staley Mehmet Sarikaya/Candan Tamerler Michael Lagunoff Roger Bumgarner Wesley Van Voorhis Collaborators: