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 ~4500 in bacteria Several thousand distinct sequence families

STRUCTURE A few thousand distinct structural folds

FUNCTION Tens of thousands of functions

EXPRESSION Different expression patterns based on time and location

INTERACTION Interaction and expression are interdependent with structure and function

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

PROTEIN FOLDING …-L-K-E-G-V-S-K-D-… …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… One amino acid Gene Protein sequence Unfolded protein Native biologically relevant state Spontaneous self-organisation (~1 second) Unique shape Precisely ordered Stable/functional Globular/compact Helices and sheets Not unique Mobile Inactive Expanded Irregular

STRUCTURE 0246 ACCURACY Experiment (X-ray, NMR) Computation (de novo) Computation (template-based) Hybrid (Iterative Bayesian interpretation of noisy NMR data with structure simulations) One distance constraint for every six residues One distance constraint for every ten residues C α RMSD

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

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

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

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, ,807 1,045,622 S. cerevisiae 5,801 5, ,505 2,456 O.sativa (6) 125,568 19, , ,990 E. coli 4, ,980 54,619

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

INFRASTRUCTURE Guerquin/Frazier ~500,000 molecules over 50+proteomes served using a 1.2 TB PostgreSQL database and a sophisticated AJAX webapplication and XML-RPC API

INFRASTRUCTURE Guerquin/Frazier

INFRASTRUCTURE Chang/Rashid

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%) ,102 O. sativa indica BGI ,925 22,278 (55%) ,149 O. sativa japonica Syngenta 38,071 20,874 (55%) ,640 O. sativa indica IRGSP 36,658 20,481 (56%) ,118 O. sativa japonica nrKOME cDNAs 19, (39%) ,793 O. sativa indica BGI pa64 37,712 15,286 (41%) ,779 Total 198,298 98,238 (50%) 31, ,581 Total (unique) 125,568 60,272 (48%) 19, , McDermott/Wichadakul

APPLICATION: RICE INTERACTOMICS BGI/McDermott

APPLICATION: DRUG DISCOVERY HSV KHSVCMV Jenwitheesuk

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

APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

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

ACKNOWLEDGEMENTS 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 Current group members: Aaron Chang David Nickle Duangdao Wichadukul Duncan Milburn Jason McDermott Marissa LaMadrid Kai Wang Kristina Montgomery Shing-Chung Ngan Vanessa Steinhilb Yi-Ling Cheng Past group members:

ACKNOWLEDGEMENTS 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 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. Collaborators:

E. coli INTERACTIONS McDermott

M. tuberculosis INTERACTIONS McDermott

C. elegans INTERACTIONS McDermott

H. sapiens INTERACTIONS McDermott

Network-based annotation for C. elegans McDermott

Articulation points KEY PROTEINS IN ANTHRAX

HOST PATHOGEN INTERACTIONS McDermott