Date post: | 21-Jan-2016 |
Category: |
Documents |
Upload: | brent-sanders |
View: | 214 times |
Download: | 0 times |
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