MODELLING INTERACTOMES

RAM SAMUDRALAASSOCIATE PROFESSOR

UNIVERSITY OF WASHINGTON

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

How can we use this information to improvehuman health and quality of life?

PROTEOME

~60,000 in human

~60,000 in rice

~4500 in bacteria

Several thousanddistinct sequencefamilies

STRUCTURE

A few thousanddistinct structuralfolds

FUNCTION

Tens of thousandsof functions

EXPRESSION

Different expressionpatterns based ontime and location

INTERACTION

Interaction andexpression are interdependentwith structure andfunction

PROTEIN FOLDING

…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…Gene

…-L-K-E-G-V-S-K-D-…

One amino acidProtein sequence

Unfolded protein

Native biologicallyrelevant 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 biologicallyrelevant state

Spontaneous self-organisation (~1 second)

• Unique shape• Precisely ordered• Stable/functional• Globular/compact• Helices and sheets

• Not unique• Mobile• Inactive• Expanded• Irregular

STRUCTURE

0 2 4 6

ACCURACYExperiment

(X-ray, NMR) Computation(de novo)

Computation(template-based)

Hybrid(Iterative Bayesian interpretation of noisy NMR data

with structure simulations)

One distance constraintfor every six residues

One distance constraintfor every ten residues

Cα RMSD

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 TRIs

H. 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

SYSTEMS

McDermott/Rashid/Wichadakul

Combining protein-protein and protein-DNA interaction networks to determine regulatory circuits

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

Guerquin/Frazier

INFRASTRUCTURE

Chang/Rashid

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

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 110,118O. sativa japonica nrKOME cDNAs 19,057 7478 (39%) 3047 38,793O. 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.eduhttp://protinfo.compbio.washington.edu

McDermott/Wichadakul

APPLICATION: RICE INTERACTOMICS

BGI/McDermott

APPLICATION: DRUG DISCOVERY

HSV KHSVCMV

Jenwitheesuk

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

+

Computationalbiology

+

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

McDermott

Articulation points

KEY PROTEINS IN ANTHRAX

HOST PATHOGEN INTERACTIONS

McDermott