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?

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.

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

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

…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… PROTEIN FOLDING 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 Native biologically relevant state Spontaneous self-organisation (~1 second) Unique shape Precisely ordered Stable/functional Globular/compact Helices and sheets

STRUCTURE 2 4 6 ACCURACY One distance constraint for every six residues for every ten residues Computation (de novo) Experiment (X-ray, NMR) Computation (template-based) 2 4 6 ACCURACY Cα RMSD Hybrid (Iterative Bayesian interpretation of noisy NMR data with structure simulations)

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

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

INTERACTION 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 McDermott/Wichadakul/Staley/Horst/Manocheewa/Jenwitheesuk/Bernard

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 McDermott/Wichadakul

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

INFRASTRUCTURE http://bioverse.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 http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu Guerquin/Frazier

INFRASTRUCTURE Guerquin/Frazier

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

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

APPLICATION: DRUG DISCOVERY HSV CMV KHSV Jenwitheesuk

APPLICATION: DRUG DISCOVERY Mkyszta

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

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

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%) 31,127 535,581 Total (unique) 125,568 60,272 (48%) 19,810 338,783 http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu BGI/McDermott/Wichadakul

APPLICATION: RICE INTERACTOMICS BGI/McDermott

APPLICATION: NANOTECHNOLOGY Oren/Sarikaya/Tamerler

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

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

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

MODELLING PROTEIN AND PROTEOME STRUCTURE FUNCTION AT 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

Current group members: Past group members: ACKNOWLEDGEMENTS 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 Current group members: 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 Past group members:

ACKNOWLEDGEMENTS Collaborators: Funding agencies: BGI Gane Wong Jun Yu Jun Wang et al. BIOTEC/KMUTT MSE Mehmet Sarikaya Candan Tamerler UW Microbiology James Staley John Mittler Michael Lagunoff Roger Bumgarner Wesley Van Voorhis Collaborators: 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

E. coli INTERACTIONS McDermott

M. tuberculosis INTERACTIONS McDermott

C. elegans INTERACTIONS McDermott

H. sapiens INTERACTIONS McDermott

Network-based annotation for C. elegans McDermott

KEY PROTEINS IN ANTHRAX Articulation points McDermott

HOST PATHOGEN INTERACTIONS McDermott