1. 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?

2. 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.

3. 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

4. STRUCTURE
A few thousand
distinct structural
folds

5. FUNCTION
Tens of thousands
of functions

6. EXPRESSION
Different expression
patterns based on
time and location

7. INTERACTION
Interaction and
expression are
interdependent
with structure and
function

8. …-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)

9. …-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

10. 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)

11. 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

12. 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

13. 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

14. 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 , , ,807 1,045,622
S. cerevisiae , , , ,456
O.sativa (6) , , , ,990
E. coli , , ,619
McDermott/Wichadakul

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

16. 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
Guerquin/Frazier

17. INFRASTRUCTURE
Guerquin/Frazier

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

19. 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

20. APPLICATION: DRUG DISCOVERY
HSV
CMV
KHSV
Jenwitheesuk

21. APPLICATION: DRUG DISCOVERY
Mkyszta

22. 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

23. 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

24. APPLICATION: RICE INTERACTOMICS
Proteome Number Number Number Number
of annotated in of
proteins (%) protein protein
network interactions
O. sativa japonica KOME cDNAs , ,841 (44%) ,102
O. sativa indica BGI , ,278 (55%) ,149 O. sativa japonica Syngenta , ,874 (55%) ,640
O. sativa indica IRGSP , ,481 (56%) ,118
O. sativa japonica nrKOME cDNAs , (39%) ,793
O. sativa indica BGI pa , ,286 (41%) ,779
Total , ,238 (50%) 31, ,581 Total (unique) , ,272 (48%) 19, ,783
BGI/McDermott/Wichadakul

25. APPLICATION: RICE INTERACTOMICS
BGI/McDermott

26. APPLICATION: NANOTECHNOLOGY
Oren/Sarikaya/Tamerler

27. 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

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

29. APPLICATION: AMELOGENIN
Signal Region
Exon 4
MGTWILFACLLGAAFAMPLPPHPGSPGYINLSYEKSHSQAINTDRTALVLTPLKWYQSMIRQPYPSYGYEPMGGWLHHQIIPVLSQQHPPSHTLQPHHHLPVVPAQQPVA
PQQPMMPVPGHHSMTPTQHHQPNIPPSAQQPFQQPFQPQAIPPQSHQPMQPQSPLHPMQPLAPQPPLPPLFSMQPLSPILPELPLEAWPATDKTKREEVD
Horst/Oren/Cheng/Wang

30. 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

31. 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:

32. 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

34. E. coli INTERACTIONS
McDermott

35. M. tuberculosis INTERACTIONS
McDermott

36. C. elegans INTERACTIONS
McDermott

37. H. sapiens INTERACTIONS
McDermott

38. Network-based annotation for C. elegans
McDermott

39. KEY PROTEINS IN ANTHRAX
Articulation points
McDermott

40. HOST PATHOGEN INTERACTIONS
McDermott