1. How does the genome of an organism
Modelling proteomes
Conjoint 548A
Ram Samudrala
How does the genome of an organism
specify its behaviour
and characteristics?

2. Proteome – all proteins of a particular system
~60,000 in human
~60,000 in rice
~4500 in bacteria
like Salmonella and
E. coli
Several thousand
distinct sequence
families

3. Modelling proteomes – understand the structure of individual proteins
A few thousand
distinct structural
folds

4. Modelling proteomes – understand their individual functions
Thousands of
possible functions

5. Modelling proteomes – understand their expression
Different expression
patterns based on
time and location

6. Modelling proteomes – understand their interactions
Interactions and
expression patterns
are interdependent
with structure and
function

7. Protein folding Gene Protein sequence Unfolded protein
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
Protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
not unique
mobile
inactive
expanded
irregular
Unfolded protein
Native biologically
relevant state
spontaneous self-organisation
(~1 second)

8. Protein folding Gene Protein sequence Unfolded protein
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
Protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
not unique
mobile
inactive
expanded
irregular
Unfolded protein
Native biologically
relevant state
spontaneous self-organisation
(~1 second)
unique shape
precisely ordered
stable/functional
globular/compact
helices and sheets

9. Protein primary structure
twenty types of amino acids R H C OH O N Cα
two amino acids join by forming a peptide bond R Cα H C O N OH R Cα H C O N c f y
each residue in the amino acid main chain has two degrees of freedom (f and y)
the amino acid side chains can have up to four degrees of freedom (c1-4)

10. Protein secondary structureb a L
f 0
0 y
+180
-180
many f,y combinations are not possible
b sheet (anti-parallel) N C N C
b sheet (parallel)
a helix

11. Protein tertiary and quaternary structures
Ribonuclease inhibitor (2bnh)
Haemoglobin (1hbh)
Hemagglutinin (1hgd)

12. Methods for obtaining structure
Experimental
X-ray crystallography
NMR spectroscopy
Theoretical
De novo prediction
Homology modelling

13. Computer representation of protein structure
Structures are stored in the protein data bank (PDB), a repository of mostly
experimental models based on X-ray crystallographic and NMR studies
<
Atoms are defined by their Cartesian coordinates:
ATOM N GLU
ATOM CA GLU
ATOM C GLU
ATOM O GLU
ATOM CB GLU
ATOM CG GLU
ATOM CD GLU
ATOM OE1 GLU
ATOM OE2 GLU
ATOM N PHE
ATOM CA PHE
These structures provide the basis for most of theoretical work in protein folding and
protein structure prediction

14. Comparison of protein structures
Need ways to determine if two protein structures are related and to compare predicted
models to experimental structures
Commonly used measure is the root mean square deviation (RMSD) of the Cartesian
atoms between two structures after optimal superposition (McLachlan, 1979):
Usually use Ca atoms
3.6 Å
2.9 Å
NK-lysin (1nkl)
Bacteriocin T102/as48 (1e68)
T102 best model
Other measures include contact maps and torsion angle RMSDs

15. 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

16. Requirements for sampling methods and scoring functions
Sampling methods must produce good decoy sets that are comprehensive and include
several native-like structures
Scoring function scores must correlate well with RMSD of conformations (the better
the score/energy, the lower the RMSD)

17. Semi-exhaustive segment-based folding
EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
minimise
Find the most protein-like structures …
generate
Make random moves to optimise
what is observed in known structures …
filter
all-atom pairwise interactions, bad contacts
compactness, secondary structure,
consensus of generated conformations

18. Critical Assessment of protein Structure Prediction methods (CASP)
Pre-CASP
CASP
Bias towards known structures
Blind prediction

19. 5.0 Å Cα RMSD for all 53 residues
CASP6 prediction (model1) for T0215
5.0 Å Cα RMSD for all 53 residues
Ling-Hong Hung/Shing-Chung Ngan

20. 4.3 Å Cα RMSD for all 70 residues
CASP6 prediction (model1) for T0281
4.3 Å Cα RMSD for all 70 residues
Sheets –no copying- pairing and packing
Ling-Hong Hung/Shing-Chung Ngan

21. Homologous proteins share similar structures
Gan et al, Biophysical Journal 83: , 2002

22. 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

23. 1.3 Å Cα RMSD for all 137 residues (80% ID)
CASP6 prediction (model1) for T0231
1.3 Å Cα RMSD for all 137 residues (80% ID)
Tianyun Liu

24. 2.4 Å Cα RMSD for all 142 residues (46% ID)
CASP6 prediction (model1) for T0271
2.4 Å Cα RMSD for all 142 residues (46% ID)
Tianyun Liu

25. Protein structure from combining theory and experiment
Ling-Hong Hung

26. Similar global sequence or structure does not imply similar function
TIM barrel
proteins
2246 with
known structure
hydrolase
ligase
lyase
oxidoreductase
transferase

27. Function prediction from structure
Kai Wang

28. Prediction of HIV-1 protease-inhibitor binding energies with MD
Can predict resistance/susceptibility to six FDA approved inhibitors with 95% accuracy in conjunction with knowledge-based methods
Ekachai Jenwitheesuk

29. Prediction of protein inhibitors
Ekachai Jenwitheesuk

30. Prediction of protein interaction networks
Target proteome
protein A
85%
predicted
interaction
protein B
90%
Interacting protein database
protein a
protein b
experimentally
determined
interaction
Assign confidence based on similarity and strength of interaction
Key paradigm is the use of homology to transfer information
across organisms; not limited to yeast, fly, and worm
Consensus of interactions helps with confidence assignments
Jason McDermott

31. E. coli predicted protein interaction network
Jason McDermott

32. M. tuberculosis predicted protein interaction network
Jason McDermott

33. C. elegans predicted protein interaction network
Jason McDermott

34. H. sapiens predicted protein interaction network
Jason McDermott

35. Bioverse – v2.0
Michal Guerquin/Zach Frazier

36. Network-based annotation for C. elegans
Jason McDermott

37. Identifying key proteins on the anthrax predicted network
Articulation point proteins
Jason McDermott

38. Identification of virulence factors
Jason McDermott

39. Bioverse - Integrator
Aaron Chang/Imran Rashid

40. + + Computational Functional Structural biology genomics genomics
Where is all this going?
Computational
biology +
Structural
genomics
Functional
genomics +
Take home message
Prediction of protein structure, function, and
networks may be used to model whole genomes to
understand organismal function and evolution

41. Current group members: Past group members:
Acknowledgements
Andrew Nichols
Baishali Chanda
Chuck Mader
David Nickle
Duangdao Wichadakul
Duncan Milburn
Ersin Emre Oren
Ekachai Jenwitheesuk
Gong Cheng
Jason McDermott
Jeremy Horst
Kai Wang
Ling-Hong Hung
Michal Guerquin
Shing-Chung Ngan
Somsak Phattarasukol
Stewart Moughon
Tianyun Liu
Weerayuth Kittichotirat
Zach Frazier
Kristina Montgomery, Program Manager
Current group members:
Aaron Chang
Marissa LaMadrid
Mike Inouye
Sarunya Suebtragoon
Past group members:
Funding agencies:
National Institutes of Health
National Science Foundation
Searle Scholars Program
Puget Sound Partners in Global Health
UW Advanced Technology Initiative