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

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

7. 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

8. INFRASTRUCTURE Guerquin/Frazier

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

10. APPLICATION: DRUG DISCOVERY Drug discovery as undertaken by the pharmaceutical company is time consuming and expensive, with very low hit rates for the amount of resources expended. Computational screening of compounds against structures of protein targets offers a way to speed up discovery time and reduce costs, but such techniques have typically had low accuracy and need high resolution structures. We will capitalise on advances in computational protein structure prediction and protein docking to improve accuracy of target-based in silico compound screening.

11. APPLICATION: DRUG DISCOVERY HSV KHSVCMV Jenwitheesuk

12. 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

13. Multitarget inhibition of Plasmodium falciparum proteins Ekachai Jenwitheesuk/Wesley Van Voorhis

14. Multi-target inhibition of Plasmodium falciparum proteins Ekachai Jenwitheesuk/Wesley Van Voorhis We experimentally evaluated 16 of our top predictions against P. falciparum in cell culture. 6/16 had an ED50 of  1  M, with the best inhibitor having an ED50 of 127nM. A negative control of 5 randomly selected compounds predicted to not inhibit our fourteen targets did not inhibit P. falciparum growth. Chong et al. 1 experimentally screened 2687 compounds and found 87 inhibitors against P. falciparum. Weisman et al. 2 screened 2162 compounds found 72 inhibitors. Their hit rates are 3.2% (87/2687) and 3.3% (72/2162). We are thus able to obtain a much higher hit rate of 38% (6/16) for a fraction of the cost: Only 16 compounds costing ~$1000 needed to be tested. Computation is fully automated and takes only a few days. Examining overlap between our computational library and their experimental libraries resulted in 75 compounds of which we would have tested 15. 8/15 inhibitors had an ED50 of  1  M, resulting in a hit rate of 53%. 1 Nat Chem Biol 2: 415-6, 2006. 2 Chem Biol Drug Des 409-16, 2006.

15. Other work and future directions Our predicted inhibitors against the dengue virus are more efficacious in cell culture than previously identified inhibitors We have predicted inhibitors against more than 100 protein targets for over 20 diseases, including HIV, SARS, Leishmania, Tuberculosis, and Influenza. Experimental testing is underway against some of the pathogens responsible. Computationally screen structurally-related compounds to experimentally verified inhibitors from a much larger library of 1 million compounds. Use data from experimental studies to figure out when our predicted inhibitors are likely to be cell-active and drug-like in their behaviour; use machine learning approaches to learn from compound characteristics (PK, ADME, toxicity), importance of protein targets, predicted binding energies and experimental inhibition. Works due to the use of a combination of knowledge- and biophysics-based methods for computational simulation.

16. APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

17. APPLICATION: SHOTGUN STRUCTURAL PROTEOMICS Add crosslinkers Repeat using different crosslinkers and isotope labelling Identify crosslinked fragments MS MKRS LVKQ VSKNT KEVN Confirm sequence MS Identify proteins with single crosslinks and fragment MS

18. 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: