Innovative Protein Inhibitor Prediction Technology

1. Samudrala group - overall research areas PROTEIN STRUCTURE PREDICTION PROTEIN FUNCTION PREDICTION CASP6 prediction for T0281 4.3 Å Cα RMSD for all 70 residues INTEGRATIVE SYSTEMS BIOLOGY PROTEIN INTERACTION PREDICTION CASP6 prediction for T0271 2.4 Å Cα RMSD for all 142 residues (46% ID) PROTEOME APPLICATION http://protinfo.compbio.washington.edu http://bioverse.compbio.washington.edu

2. Samudrala group – specific applications PROTEIN INHIBITOR PREDICTION PROTEIN DESIGN/BIONANOTECHNOLOGY

3. Technology for the discovery of inhibitors • Identify protein targets where the three-dimensional structure can be elucidated. • Prioritise targets based on Bioverse networks and functional annotation. • Computationally predict inhibitors against the targets: • - small molecule inhibitors are predicted using our docking with dynamics protocol • to screen a library of FDA approved and experimental compounds used for other • indications. • - peptide inhibitors are predicted using our all-atom energy function to identify and • design peptide sequences that have a strong binding energy. • Lowered risk for drug development since extensive toxicology profiles are • available for most of these compounds. • Other collections of compounds could also be screened to fit specific collaborative • programs. • Make derivatives of the best inhibitors and computationally determine binding • affinities. • Screen other targets using the best inhibitors to determine potential side effects and • cross-reactivity.

4. Outcome of technologies • HIV protease and RT drug resistance prediction • - most accurate drug resistance prediction method when combined with knowledge- • based methods (published) • HIV gp41 peptide inhibitor prediction (published) • HIV integrase inhibitor predictions (in progress) • Malaria multi-target inhibitor prediction • - found 5-6 known antimalarials in a screen of 14 targets (published) • Herpesvirus broad spectrum inhibitor prediction • - found one inhibitor experimentally validated in cell culture against CMV, HSV, • and KHSV (in progress) • Inhibitor discovery and analysis for various other diseases • - SARS, CMV (published) • -Trypanosomal infection, Leishmania, avian and dog influenza • HIV opportunistic pathogens, cancer (in progress) Ekachai Jenwitheesuk

5. Predicted inhibitor against CMV, HSV, and KHSV proteases Ekachai Jenwitheesuk

6. Predicted inhibitor against CMV protease

7. On to experimental validation studies by Michael Lagunoff

8. What we have • A robust technology for predicting protein structures. • A generalisable technology for predicting potential protein inhibitors. • Putative inhibitors of tens of disease targets. • A demonstration of the value of the technology for herpesvirus infection. • A potential drug development opportunity. Businesses that could be created • Herpes therapeutic development. • Therapeutic discovery in collaboration with drug development companies. • Second option requires validation made possible by the first.

9. What’s required in the short term • Need to show that our inhibitor works in mouse models of herpes. • Need to measure dissociation constants (Kd) between our inhibitor and target • proteases. What’s required in the longer term • Drug discovery: • Need lots of computers to do screening for specific targets, especially if we • partner with drug development companies. • Drug development: • Need resources for in vitro validation of predicted inhibitors. • Need resources for in vivo validation. • Need resources for clinical development.

11. Multi-target multi-disease therapeutic discovery – small molecules • Disease A • Protein A1 • Protein A2 • Protein A3 • … • … • Disease B • Protein B1 • Protein B2 • Protein B3 • … • … • Disease C • Protein C1 • Protein C2 • Protein C3 • … • … • Disease XXX • Protein XXX1 • Protein XXX2 • Protein XXX3 • … • … Screen library of FDA approved or experimental compounds using docking with dynamics protocol Binding affinity calculation using docking with dynamics protocol Disease A Disease B Disease C Disease XXX Protein A… Protein A2 Protein A1 1 … 2 … 3 … 4 Inhibitor X 5 … 6 … Protein B… Protein B2 Protein B1 1 … 2 Inhibitor X 3 … 4 … 5 … 6 … Protein C… Protein C2 Protein C1 1 … 2 … 3 … 4 … 5 Inhibitor X 6 … Protein XXX… Protein XXX2 Protein XXX1 1 … 2 … 3 Inhibitor X 4 … 5 … 6 … Rank of inhibitory concentration . . . . . . . . . . . Ekachai Jenwitheesuk More than a dozen publications.

12. Multi-target multi-disease therapeutic discovery – peptides • Disease A • Protein A1 • Protein A2 • Protein A3 • … • … • Disease B • Protein B1 • Protein B2 • Protein B3 • … • … • Disease C • Protein C1 • Protein C2 • Protein C3 • … • … • Disease XXX • Protein XXX1 • Protein XXX2 • Protein XXX3 • … • … Find high stability regions on surface of a protein structure; design high stability variants using all-atom function Stability calculation using all-atom scoring function Disease A Disease B Disease C Disease XXX Protein A… Protein A2 Protein A1 1 … 2 … 3 … 4 Inhibitor X 5 … 6 … Protein B… Protein B2 Protein B1 1 … 2 Inhibitor X 3 … 4 … 5 … 6 … Protein C… Protein C2 Protein C1 1 … 2 … 3 … 4 … 5 Inhibitor X 6 … Protein XXX… Protein XXX2 Protein XXX1 1 … 2 … 3 Inhibitor X 4 … 5 … 6 … Rank of all-atom score . . . . . . . . . . . Ekachai Jenwitheesuk Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 893-900, 2005.

13. Prediction of HIV-1 protease-inhibitor binding energies Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 157-166, 2005. Jenwitheesuk E, Samudrala R. BMC Structural Biology 3: 2, 2003. Ekachai Jenwitheesuk

14. Identification of multi-target inhibitors against malaria Ekachai Jenwitheesuk Jenwitheesuk E, Samudrala R. Journal of the American Medical Association 294: 1490-1491, 2005.

15. Prediction of HIV inhibitor resistance/susceptibility http://protinfo.compbio.washington.edu/pirspred/ Ekachai Jenwitheesuk/ Kai Wang/John Mittler Jenwitheesuk E, Wang K, Mittler J, Samudrala R. AIDS 18: 1858-1859, 2004. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Trends in Microbiology 13: 150-151, 2005.

16. Summary of our multi-target multi-disease drug discovery efforts Dengue – 3 targets HIV – 5 targets Influenza – protease inhibitors for 4 strains Leishmania – 4 targets M. tuberculosis – 30 targets Plasmodium – 14 targets SARS – protease inhibitor published T. cruz – 15 targets T. brucei – 14 targets Herpesviruses – protease inhibitors for 5 strains Cancer – 31 targets Ekachai Jenwitheesuk

17. Summary of our multi-target multi-disease drug discovery efforts Ekachai Jenwitheesuk

18. What do we want to do: Ideal world scenario Focus on discovery of inhibitors for third world diseases Foster an “open drug” development approach where discoveries are rapidly published Build on infrastructure created by drug companies to validate and deliver therapeutics to the people who need it

19. What do we want to do: Specifics Package HIV drug resistance prediction server into a standalone tool for use in a clinical setting Screen drugs computationally for more targets and diseases most relevant to global health Perform in vitro assays of drugs being predicted with collaborators (SBRI, UW, UCSF) Perform in vivo studies Conduct clinical trials to ensure follow through of leads

20. Future (long-term) applications of our research Screen host (human) proteins for side effects Drug target discovery using the Bioverse framework Personalised drugs based on SNP discovery Integrate with high-resolution structure elucidation Use protein design/nanotechnology for targeted delivery

21. Funding specifics Probabily of success is higher due to: Multi-target inhibition Mechanism of action is understood Use of preapproved drugs Side effects may be predicted Costs are reduced due to: Computational discovery Use of preapproved drugs Lower number of failed drugs