MODELLING PROTEOMES

RAM SAMUDRALAASSOCIATE PROFESSOR

UNIVERSITY OF WASHINGTON

How does the genome of an organism specifyits behaviour and characteristics?

STRUCTURE

0.5 Å Cα RMSD for 173 residues (60% identity)

T0290 – peptidyl-prolyl isomerase from H. sapiens

T0364 – hypothetical from P. putida

5.3 Å Cα RMSD for 153 residues (11% identity)

T0332 – methyltransferase from H. sapiens

2.0 Å Cα RMSD for 159 residues (23% identity)

T0288 – PRKCA-binding from H. sapiens

2.2 Å Cα RMSD for 93 residues (25% identity)

Liu/Hong-Hung/Ngan

FUNCTION

Wang/Cheng

Ion binding energyprediction 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 signatureaccuracy

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 TRIsH. sapiens 26,741 17,652 828,807 1,045,622S. cerevisiae 5,801 5,175 192,505 2,456O.sativa (6) 125,568 19,810 338,783 439,990E. coli 4,208 885 1,980 54,619

INFRASTRUCTURE

Guerquin/Frazier

http://bioverse.compbio.washington.eduhttp://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

INFRASTRUCTURE

Chang/Rashid

http://bioverse.compbio.washington.edu/integrator

APPLICATION: DRUG DISCOVERY

HSV KHSVCMV

Computionally predicted broad spectrum human herpesvirus protease inhibitors is effective in vitroagainst members from all three classes and is comparable or better than anti-herpes drugs

HSVHSV

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

Structuralgenomics

Functionalgenomics

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Computationalbiology

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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 •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: