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

2. ? Rationale for understanding protein structure and function
structure determination
structure prediction
Protein sequence
-large numbers of
sequences, including
whole genomes
Protein structure
- three dimensional
- complicated
- mediates function ?
homology
rational mutagenesis
biochemical analysis
model studies
Protein function
- rational drug design and treatment of disease
- protein and genetic engineering
- build networks to model cellular pathways
- study organismal function and evolution

3. Protein folding DNA protein sequence unfolded protein native state
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
not unique
mobile
inactive
expanded
irregular
unfolded protein
native state
spontaneous self-organisation
(~1 second)

4. Protein folding DNA protein sequence unfolded protein native state
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
not unique
mobile
inactive
expanded
irregular
unfolded protein
native state
spontaneous self-organisation
(~1 second)
unique shape
precisely ordered
stable/functional
globular/compact
helices and sheets

5. 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

6. Semi-exhaustive segment-based folding
EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
minimise
monte carlo with simulated annealing
conformational space annealing, GA …
generate
fragments from database
14-state f,y model …
filter
all-atom pairwise interactions, bad contacts
compactness, secondary structure

7. Decomposition of all-atom function using ICA
(blind separation of sources by maximising the statistical independence across various channels)
atom type 2
atom type 1
energy
distance (A)
Disulphide
bridges
atom type 1
atom type 2
distance (A)
energy
Main chain
hydrogen bonding
atom type 2
atom type 1
energy
distance (A)
Salt
bridges
atom type 2
atom type 1
energy
distance (A)
Side -> main chain
hydrogen bonding
Shing-Chung Ngan

8. Ab initio prediction at CASP
Before CASP (BC):
“solved”
(biased results)
CASP1: worse than
random
CASP2: worse than
random with one
exception
CASP3: consistently predicted correct topology - ~ 6.0 Å for 60+ residues
CASP4: consistently predicted correct topology - ~4-6.0 A for residues
**T97/er29 – 6.0 Å (80 residues; 18-97)
*T98/sp0a – 6.0 Å (60 residues; )
**T102/as48 – 5.3 Å (70 residues; 1-70)
**T106/sfrp3 – 6.2 Å (70 residues; 6-75)
**T110/rbfa – 4.0 Å (80 residues; 1-80)
*T114/afp1 – 6.5 Å (45 residues; 36-80)

9. Prediction for CASP4 target T110/rbfa
Ca RMSD of 4.0 Å for 80 residues (1-80)

10. Prediction for CASP4 target T97/er29
Ca RMSD of 6.2 Å for 80 residues (18-97)

11. Prediction for CASP4 target T106/sfrp3
Ca RMSD of 6.2 Å for 70 residues (6-75)

12. Prediction for CASP4 target T98/sp0a
Ca RMSD of 6.0 Å for 60 residues (37-105)

13. Prediction for CASP4 target T126/omp
Ca RMSD of 6.5 Å for 60 residues (87-146)

14. Prediction for CASP4 target T114/afp1
Ca RMSD of 6.5 Å for 45 residues (36-80)

15. Postdiction for CASP4 target T102/as48
Ca RMSD of 5.3 Å for 70 residues (1-70)

16. CASP5?

17. 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

18. A graph theoretic representation of protein structure
residues
as nodes
-0.6 (V1)
-1.0 (F)
-0.7 (K)
-0.5 (I)
-0.9 (V2)
weigh
nodes
-0.5 (I)
-0.6 (V1)
-0.9 (V2)
-1.0 (F)
-0.7 (K)
-0.3
-0.4
-0.2
-0.1
construct
graph
-0.5 (I)
-0.9 (V2)
-1.0 (F)
-0.7 (K)
-0.3
-0.4
-0.2
-0.1
find cliques
W = -4.5

19. Comparative modelling at CASP
fair
~ 75%
~ 1.0 Å
~ 3.0 Å
CASP3
~75%
~ 2.5 Å
CASP4
~ 2.0 Å
CASP1
poor
~ 50%
> 5.0 Å BC
excellent
~ 80%
1.0 Å
2.0 Å
alignment
side chain
short loops
longer loops
CASP4: overall model accuracy ranging from 1 Å to 6 Å for 50-10% sequence identity
**T128/sodm – 1.0 Å (198 residues; 50%)
**T111/eno – 1.7 Å (430 residues; 51%)
**T122/trpa – 2.9 Å (241 residues; 33%)
**T125/sp18 – 4.4 Å (137 residues; 24%)
**T112/dhso – 4.9 Å (348 residues; 24%)
**T92/yeco – 5.6 Å (104 residues; 12%)

20. Prediction for CASP4 target T128/sodm
Ca RMSD of 1.0 Å for 198 residues (PID 50%)

21. Prediction for CASP4 target T111/eno
Ca RMSD of 1.7 Å for 430 residues (PID 51%)

22. Prediction for CASP4 target T122/trpa
Ca RMSD of 2.9 Å for 241 residues (PID 33%)

23. Prediction for CASP4 target T125/sp18
Ca RMSD of 4.4 Å for 137 residues (PID 24%)

24. Prediction for CASP4 target T112/dhso
Ca RMSD of 4.9 Å for 348 residues (PID 24%)

25. Prediction for CASP4 target T92/yeco
Ca RMSD of 5.6 Å for 104 residues (PID 12%)

26. CASP5?

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

28. Prediction for Invb from Salmonella typhimurium

29. Prediction of HIV-1 protease-inhibitor binding energies with MD
1.0
0.5
with MD
without MD
Correlation coefficient ps
MD simulation time
Ekachai Jenwitheesuk

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

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

32. Bioverse – human protein-protein interaction network
Jason Mcdermott/Zach Frazier

33. Bioverse – salmonella protein-protein interaction network
Jason Mcdermott/Zach Frazier

34. Bioverse – human protein-protein similarity network
Jason Mcdermott/Zach Frazier

35. Take home message Acknowledgements
Prediction of protein structure and function can
be used to model whole proteomes to understand
organismal function and evolution
Ekachai Jenwitheesuk
Jason McDermott
Ling-Hong Hung
Shing-Chung Ngan
Yi-Ling Chen
Zach Frazier
Group members
Levitt and Moult groups
Acknowledgements