Transcript Modelling genome structure and function

Modelling genome structure and function
Ram Samudrala
University of Washington
Rationale for understanding protein structure and function
Protein sequence
-large numbers of
sequences, including
whole genomes
?
Protein function
- rational drug design and treatment of disease
- protein and genetic engineering
- build networks to model cellular pathways
- study organismal function and evolution
structure determination
structure prediction
Protein structure
- three dimensional
- complicated
- mediates function
homology
rational mutagenesis
biochemical analysis
model studies
Protein folding
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
DNA
protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
unfolded protein
spontaneous self-organisation
(~1 second)
native state
not unique
mobile
inactive
expanded
irregular
Protein folding
…-CUA-AAA-GAA-GGU-GUU-AGC-AAG-GUU-…
DNA
protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
unfolded protein
spontaneous self-organisation
(~1 second)
native state
not unique
mobile
inactive
expanded
irregular
unique shape
precisely ordered
stable/functional
globular/compact
helices and sheets
Ab initio 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
Semi-exhaustive segment-based folding
EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
generate
…
fragments from database
14-state f,y model
…
minimise
…
monte carlo with simulated annealing
conformational space annealing, GA
…
filter
all-atom pairwise interactions, bad contacts
compactness, secondary structure
Historical perspective on ab initio prediction
Before CASP (BC):
“solved”
CASP1: worse than
random
(biased results)
CASP2: worse than
random with one
exception
CASP3: consistently predicted correct topology - ~ 6.0 Å for 60+ residues
*T56/dnab – 6.8 Å (60 residues; 67-126)
**T61/hdea – 7.4 Å (66 residues; 9-74)
**T64/sinr – 4.8 Å (68 residues; 1-68)
*T74/eps15 – 7.0 Å (60 residues; 154-213)
**T59/smd3 – 6.8 Å (46 residues; 30-75)
**T75/ets1 – 7.7 Å (77 residues; 55-131)
CASP4: ?
Prediction for CASP4 target T110/rbfa
Ca RMSD of 4.0 Å for 80 residues (1-80)
Prediction for CASP4 target T97/er29
Ca RMSD of 6.2 Å for 80 residues (18-97)
Prediction for CASP4 target T106/sfrp3
Ca RMSD of 6.2 Å for 70 residues (6-75)
Prediction for CASP4 target T98/sp0a
Ca RMSD of 6.0 Å for 60 residues (37-105)
Prediction for CASP4 target T126/omp
Ca RMSD of 6.5 Å for 60 residues (87-146)
Prediction for CASP4 target T114/afp1
Ca RMSD of 6.5 Å for 45 residues (36-80)
Postdiction for CASP4 target T102/as48
Ca RMSD of 5.3 Å for 70 residues (1-70)
Historical perspective on ab initio prediction
Before CASP (BC):
“solved”
CASP1: worse than
random
(biased results)
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 60-80+ residues
**T97/er29 – 6.0 Å (80 residues; 18-97)
*T98/sp0a – 6.0 Å (60 residues; 37-105)
**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)
Comparative modelling of protein structure
align
…
KDHPFGFAVPTKNPDGTMNLMNWECAIP
KDPPAGIGAPQDN----QNIMLWNAVIP
** * *
* *
* * *
**
build initial model
refine
…
construct non-conserved
side chains and main chains
Historical perspective on comparative modelling
BC
alignment
side chain
short loops
longer loops
excellent
~ 80%
1.0 Å
2.0 Å
Historical perspective on comparative modelling
alignment
side chain
short loops
longer loops
BC
CASP1
excellent
~ 80%
1.0 Å
2.0 Å
poor
~ 50%
~ 3.0 Å
> 5.0 Å
A graph theoretic representation of protein structure
-0.6 (V1)
represent
residues
as nodes
-0.5 (I)
-0.9 (V2)
weigh
nodes
-0.7 (K)
-1.0 (F)
construct
graph
-0.6 (V1)
-0.5 (I)
W = -4.5
-0.1
-0.3
-1.0 (F)
-0.9 (V2)
-0.1
-0.2
-0.7 (K)
find cliques
-0.5 (I)
-0.1
-0.3
-1.0 (F)
-0.9 (V2)
-0.1
-0.2
-0.7 (K)
-0.2
Prediction for CASP4 target T128/sodm
Ca RMSD of 1.0 Å for 198 residues (PID 50%)
Prediction for CASP4 target T111/eno
Ca RMSD of 1.7 Å for 430 residues (PID 51%)
Prediction for CASP4 target T122/trpa
Ca RMSD of 2.9 Å for 241 residues (PID 33%)
Prediction for CASP4 target T125/sp18
Ca RMSD of 4.4 Å for 137 residues (PID 24%)
Prediction for CASP4 target T112/dhso
Ca RMSD of 4.9 Å for 348 residues (PID 24%)
Prediction for CASP4 target T92/yeco
Ca RMSD of 5.6 Å for 104 residues (PID 12%)
Historical perspective on comparative modelling
alignment
side chain
short loops
longer loops
BC
CASP1
CASP2
CASP3
CASP4
excellent
~ 80%
1.0 Å
2.0 Å
poor
~ 50%
~ 3.0 Å
> 5.0 Å
fair
~ 75%
~ 1.0 Å
~ 3.0 Å
fair
~75%
~ 1.0 Å
~ 2.5 Å
fair
~75%
~ 1.0 Å
~ 2.0 Å
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%)
Computational aspects of structural genomics
A. sequence space
B. comparative modelling
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*
C. fold recognition
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*
*
*
*
*
E. target selection
D. ab initio prediction
F. analysis
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*
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*
*
*
*
*
*
*
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targets
(Figure idea by Steve Brenner.)
Computational aspects of functional genomics
structure based methods
microenvironment analysis
G. assign function
*
structure comparison
*
*
*
*
zinc binding site?
homology
+
sequence based methods
sequence comparison
motif searches
phylogenetic profiles
domain fusion analyses
+
experimental data
*
function?
assign function to
entire protein space
Conclusions: structure
Ab initio prediction can produce low resolution models
that may aid gross functional studies
Comparative modelling can produce high
resolution models that can be used
to study detailed function
Large scale structure prediction will complement
experimental structural genomics efforts
Conclusions:function
Detailed analysis of structures can be used to
predict protein function, complementing experimental
and sequence based techniques
Structure comparisons and microenvironment
analyses can be used to prediction function on a
genome-wide scale
Large scale function prediction will complement
experimental functional genomics efforts
Take home message
Prediction of protein structure and function can
be used to model whole genomes to understand
organismal function and evolution
Acknowledgements
Michael Levitt, Stanford University
John Moult, CARB
Patrice Koehl, Stanford University
Yu Xia, Stanford Univeristy
Levitt and Moult groups