1. University of Washington
Modelling proteomes
Ram Samudrala
University of Washington

2. { } What is a “proteome”? What does it mean to “model a proteome”?
All proteins of a particular system
(organelle, cell, organism)
What does it mean to “model a proteome”?
For any protein, we wish to:
ANNOTATION {
figure out what it looks like (structure or form)
understand what it does (function)
Repeat for all proteins in a system
EXPRESSION +
INTERACTION }
Understand the relationships between all of them

3. De novo prediction of protein structure
sample conformational space such that
native-like conformations are found
select
hard to design functions
that are not fooled by
non-native conformations
(“decoys”)
astronomically large number of conformations
5 states/100 residues = 5100 = 1070

4. CASP5 prediction for T138
4.6 Å Cα RMSD for 84 residues

5. CASP5 prediction for T146
5.6 Å Cα RMSD for 67 residues

6. 4.8 Å Cα RMSD for all 69 residues
CASP5 prediction for T170
4.8 Å Cα RMSD for all 69 residues

7. CASP5 prediction for T129
5.8 Å Cα RMSD for 68 residues

8. CASP5 prediction for T172
5.9 Å Cα RMSD for 74 residues

9. CASP5 prediction for T187
5.1 Å Cα RMSD for 66 residues

10. CASP5 independent assessor’s results

11. Comparative modelling of protein structure
KDHPFGFAVPTKNPDGTMNLMNWECAIP
KDPPAGIGAPQDN----QNIMLWNAVIP
** * * * * * * * ** …
scan
align
de novo simulation
build initial model
minimum perturbation
construct non-conserved
side chains and main chains
graph theory, semfold
refine
physical functions

12. 1.0 Å Cα RMSD for 133 residues (57% id)
CASP5 prediction for T129
1.0 Å Cα RMSD for 133 residues (57% id)

13. 1.0 Å Cα RMSD for 249 residues (41% id)
CASP5 prediction for T182
1.0 Å Cα RMSD for 249 residues (41% id)

14. 2.7 Å Cα RMSD for 99 residues (32% id)
CASP5 prediction for T150
2.7 Å Cα RMSD for 99 residues (32% id)

15. 6.0 Å Cα RMSD for 428 residues (24% id)
CASP5 prediction for T185
6.0 Å Cα RMSD for 428 residues (24% id)

16. 2.5 Å Cα RMSD for 125 residues (22% id)
CASP5 prediction for T160
2.5 Å Cα RMSD for 125 residues (22% id)

17. 6.0 Å Cα RMSD for 260 residues (14% id)
CASP5 prediction for T133
6.0 Å Cα RMSD for 260 residues (14% id)

18. Livebench 7 automated assessment for 71 targets

19. Prediction of protein-inhibitor binding energies with dynamics
HIV
protease
MD simulation time
Correlation coefficient ps
1.0
0.5
with MD
without MD
Ekachai Jenwitheesuk

20. Prediction of SARS CoV proteinase inhibitors
Ekachai Jenwitheesuk

21. Prediction of inhibitor resistance/susceptibility
Kai Wang / Ekachai Jenwitheesuk

22. } + + Integrated structural and functional annotation of proteomes
structure based methods
microenvironment analysis
zinc binding site?
structure comparison
homology function? *
Bioverse
assign function to
entire protein space
sequence based methods
sequence comparison
motif searches
phylogenetic profiles
domain fusion analyses +
experimental data
single molecule + genomic/proteomic +
EXPRESSION
INTERACTION }

23. Bioverse – explore relationships among molecules and systems
Jason McDermott

24. Bioverse – explore relationships among molecules and systems
Jason Mcdermott

25. Bioverse – prediction of protein interaction networks
Target proteome
protein A
85%
predicted
interaction
protein B
90%
Interacting protein database
protein α
protein β
experimentally
determined
interaction
Assign confidence based on similarity and strength of interaction
Jason Mcdermott

26. Bioverse – E. coli predicted protein interaction network
Jason McDermott

27. Bioverse – M. tuberculosis predicted protein interaction network
Jason McDermott

28. Bioverse – C. elegans predicted protein interaction network
Jason McDermott

29. Bioverse – H. sapiens predicted protein interaction network
Jason McDermott

30. Bioverse – network-based annotation for C. elegans
Jason McDermott

31. Bioverse – identifying key proteins on the anthrax predicted network
Articulation point proteins
Jason McDermott

32. Bioverse – identifying key proteins on the rice predicted network
Defense-related proteins
Jason McDermott

33. Bioverse – viewer
Aaron Chang

34. Take home message Prediction of protein structure, function, and
networks may be used to model whole genomes to
understand organismal function and evolution

35. Acknowledgements http://bioverse.compbio.washington.edu
Aaron Chang
Ekachai Jenwitheesuk
Gong Cheng
Jason McDermott
Kai Wang
Ling-Hong Hung
Lynne Townsend
Marissa LaMadrid
Mike Inouye
Stewart Moughon
Shing-Chung Ngan
Tianyun Liu
Yi-Ling Cheng
Zach Frazier
National Institutes of Health
National Science Foundation
Searle Scholars Program (Kinship Foundation)
UW Advanced Technology Initative in Infectious Diseases