Transcript THERAPUETIC DISCOVERY BY MODELLING

THERAPUETIC DISCOVERY BY MODELLING INTERACTOMES
RAM SAMUDRALA
ASSOCIATE PROFESSOR
UNIVERSITY OF WASHINGTON
How does the genome of an organism specify its behaviour and
characteristics?
How can we capitalise on this information to discover therapeutics to
improve human health and quality of life?
STRUCTURE
T0290 – peptidyl-prolyl isomerase from H. sapiens
T0288 – PRKCA-binding from H. sapiens
0.5 Å Cα RMSD for 173 residues (60% identity)
2.2 Å Cα RMSD for 93 residues (25% 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
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
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/BtubB interolog model from P. dejongeii
(35% identity to eukaryotic tubulins)
McDermott/Wichadakul/Staley/Horst/Manocheewa/Jenwitheesuk/Bernard
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
SYSTEMS
Combining protein-protein and protein-DNA interaction networks to determine regulatory circuits
McDermott/Wichadakul
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
INFRASTRUCTURE
Guerquin/Frazier
INFRASTRUCTURE
http://bioverse.compbio.washington.edu/integrator
Chang/Rashid
APPLICATION: DRUG DISCOVERY
Drug discovery as undertaken by the pharmaceutical company is time
consuming and expensive, with very low hit rates for the amount of
resources expended.
Computational screening of compounds against structures of protein targets
offers a way to speed up discovery time and reduce costs, but such
techniques have typically had low accuracy and need high resolution
structures.
We will capitalise on advances in computational protein structure prediction
and protein docking to improve accuracy of target-based in silico compound
screening.
APPLICATION: DRUG DISCOVERY
HSV
CMV
KHSV
Jenwitheesuk
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
HSV
KHSV
CMV
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
Multitarget inhibition of Plasmodium falciparum proteins
Ekachai Jenwitheesuk/Wesley Van Voorhis
Multi-target inhibition of Plasmodium falciparum proteins
We experimentally evaluated 16 of our top predictions against P. falciparum
in cell culture. 6/16 had an ED50 of  1 M, with the best inhibitor having an
ED50 of 127nM.
A negative control of 5 randomly selected compounds predicted to not inhibit
our fourteen targets did not inhibit P. falciparum growth.
Chong et al.1 experimentally screened 2687 compounds and found 87
inhibitors against P. falciparum. Weisman et al.2 screened 2162 compounds
found 72 inhibitors. Their hit rates are 3.2% (87/2687) and 3.3% (72/2162).
We are thus able to obtain a much higher hit rate of 38% (6/16) for a fraction
of the cost: Only 16 compounds costing ~$1000 needed to be tested.
Computation is fully automated and takes only a few days.
Examining overlap between our computational library and their experimental
libraries resulted in 75 compounds of which we would have tested 15. 8/15
inhibitors had an ED50 of  1M, resulting in a hit rate of 53%.
1Nat
Chem Biol 2: 415-6, 2006.
Biol Drug Des 409-16, 2006.
2Chem
Ekachai Jenwitheesuk/Wesley Van Voorhis
Other work and future directions
Our predicted inhibitors against the dengue virus are more efficacious in cell
culture than previously identified inhibitors
We have predicted inhibitors against more than 100 protein targets for over
20 diseases, including HIV, SARS, Leishmania, Tuberculosis, and Influenza.
Experimental testing is underway against some of the pathogens
responsible.
Computationally screen structurally-related compounds to experimentally
verified inhibitors from a much larger library of 1 million compounds.
Use data from experimental studies to figure out when our predicted
inhibitors are likely to be cell-active and drug-like in their behaviour; use
machine learning approaches to learn from compound characteristics (PK,
ADME, toxicity), importance of protein targets, predicted binding energies
and experimental inhibition.
Works due to the use of a combination of knowledge- and biophysics-based
methods for computational simulation.
APPLICATION: NANOTECHNOLOGY
Oren/Sarikaya/Tamerler
APPLICATION: SHOTGUN STRUCTURAL PROTEOMICS
MS
Identify proteins with single
crosslinks and fragment
MS
Identify crosslinked
fragments
Add crosslinkers
MKRS VSKNT
MS
LVKQ
KEVN
Confirm sequence
Repeat using different crosslinkers and isotope labelling
ACKNOWLEDGEMENTS
Current group members:
Past group members:
•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
•Aaron Chang
•Duncan Milburn
•Jason McDermott
•Kai Wang
•Marissa LaMadrid
Collaborators:
•James Staley
•Mehmet Sarikaya/Candan Tamerler
•Michael Lagunoff
•Roger Bumgarner
•Wesley Van Voorhis
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