MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON

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. MOTIVATION The functions necessary for life are undertaken by proteins and their interactions (with other proteins, DNA, RNA, and small molecules). Protein function is mediated by atomic three dimensional structure. Knowing protein structure at atomic resolution will therefore enable us to: Determine and understand molecular function. Understand substrate and ligand binding interactions. Devise intelligent mutagenesis and biochemical. experiments to understand biological function. Design therapeutics rationally. Design novel proteins. Knowing the atomic level structures of all proteins encoded by an organism’s genome, along with interactions, will enable us to understand the underlying mechanistic basis or “wiring diagram” that comprises pathways, systems, and organisms. Applications in the area of medicine, nanotechnology, and biological computing.

3. PROTEOME ~30,000 in human ~60,000 in rice ~4500 in bacteria countless numbers total Several thousand distinct sequence families

4. STRUCTURE A few thousand distinct structural folds

5. FUNCTION Tens of thousands of functions

6. EXPRESSION Different expression patterns based on time and location

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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

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

16. 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

17. INFRASTRUCTURE Guerquin/Frazier

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

19. APPLICATION: DRUG DISCOVERY SINGLE TARGET SCREENING MULTITARGET SCREENING Disease Target identification Single disease related protein Multiple disease related proteins Compound library Small molecule library DRUG-LIKE COMPOUNDS High throughput screening Computational screening Computational screening with dynamics Initial candidates Initial candidates Experimental verification Experimental verification Experimental verification Success rate+Success rate ++ Success rate +++++ Time++++ Time +++ Time +++ Cost++++ Cost +++ Cost + Jenwitheesuk

20. APPLICATION: DRUG DISCOVERY CMV KHSV HSV Jenwitheesuk

21. APPLICATION: DRUG DISCOVERY

22. 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

23. APPLICATION: DRUG DISCOVERY Mkytsza

24. Predicted inhibitory constant 10-13 10-12 10-11 10-10 10-9 10-8 10-7 None Van Voorhis/Rivas/Chong/Weismann

25. 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 BGI/McDermott/Wichadakul

26. APPLICATION: RICE INTERACTOMICS BGI/McDermott

27. APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

28. APPLICATION: AMELOGENIN • Principal protein involved in enamel and hard tissue formation. • Multifunction protein: • Mineralisation. • Signaling. • Adhesion to process matrix. • Physical protein-protein interactions. • Never been crystallised (irregular/unstable?). • Most proteins with non-repeating sequence are active in globular form. • Many proteins fold into globular form upon interaction with substrate / interactor. • Assumption of linear and globular forms. • Goal is to understand protein structure, function, binding to hydroxyapatite, interaction with other amelogenin molecules, and formation of enamel and hard tissue formation

29. APPLICATION: AMELOGENIN Predicted five models (typical for CASP). Annotate structure with experimental and simulation evidence to find best predicted globular structure and infer function.

30. APPLICATION: AMELOGENIN Signal Region Exon 4 MGTWILFACLLGAAFAMPLPPHPGSPGYINLSYEKSHSQAINTDRTALVLTPLKWYQSMIRQPYPSYGYEPMGGWLHHQIIPVLSQQHPPSHTLQPHHHLPVVPAQQPVA 1 10 20 30 40 50 60 70 80 90 100 110 PQQPMMPVPGHHSMTPTQHHQPNIPPSAQQPFQQPFQPQAIPPQSHQPMQPQSPLHPMQPLAPQPPLPPLFSMQPLSPILPELPLEAWPATDKTKREEVD 120 130 140 150 160 170 180 190 200 210 Horst/Oren/Cheng/Wang

31. 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

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

33. 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

35. E. coli INTERACTIONS McDermott

36. M. tuberculosisINTERACTIONS McDermott

37. C. elegans INTERACTIONS McDermott

38. H. sapiens INTERACTIONS McDermott

39. Network-based annotation for C. elegans McDermott

40. KEY PROTEINS IN ANTHRAX Articulation points McDermott

41. HOST PATHOGEN INTERACTIONS McDermott