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Functional Site Prediction Selects
Correct Protein Models
Vijayalakshmi Chelliah
[email protected]
Division of Mathematical Biology
National Institute for Medical Research
Mill Hill, London
Sixth International Conference on Bioinformatics InCoB2007
HKUST, Hong Kong
27th – 30th August 2007
Functional site prediction - applications:
 To predict function of the protein (Pazos &
sternberg, 2004; PNAS 101:14754-9)
 In protein – protein docking: To select the nearnative docked solution. (Chelliah et al., 2006; JMB
357:1669-82).
 In sequence-structure homology recognition and to
improve alignment accuracy (chelliah et al., 2005;
Proteins 61:722-31)
Gene sequence
Protein sequence
Xray/NMR
Protein structure
Predict structure:
De-novo/ab-initio
Protein structure
select correct models
Functional site
prediction
Overview
De-novo protein structure prediction method
(decoy generation)
Functional site prediction method
Evaluating models
Conclusions
De-novo protein structure prediction method
SEQUENCE ALIGNMENT
IDEAL FORMS
Predicted sec. structure
Predicted Res. burial
Fold Generation
* Taylor (2002).
Nature. 416:657-660
and scoring
Secondary structure
Top 1/3
‘stick’ level
C models
Threading
Top 100+N
Refinement
Top 100+N
Top 200 models
Residue level
STRUCTURE PATTERNS
Main-chain level
Functional site prediction method
 Biochemically important residues are typically found in
close proximity and are also highly conserved.
 Functional site prediction is done using CRESCENDO *
(gives scores for each residue position).
* Chelliah, V., L. Chen, et al. (2004). J Mol Biol
342(5): 1487-504.
CRESCENDO: Functional site prediction method
Alignment position
Multiple
sequence
alignment of
the homologous
sequences:
structure
based sequence
alignment
Environment
specific
substitution
table
1 2 3 4 5 6………………..
sp1
sp2
sp3
sp4
spspspN
Observed
substitution pattern
for each amino acid
(p) at tth position
*
(sp1+sp2+sp3+sp+…+sp
N)/N = Expected
substitution pattern
for each amino acid
(q) at tth position
Divergent score between
the observed (p) and
expected (q)
substitution table
* Overington et al., (1992). Protein Science 1:216-26
Assumptions
• Correct or near-native like models will have the
critical residues important for binding (identified by
CRESCENDO) to be in close proximity to each other.
i.e. Functional residues in the correct models form clusters
Functional residues in the incorrect models might be
scattered.
• Can correct and incorrect models be distinguished by
looking at how the functional residues are packed in
the models?
Clustering of models
200 decoy models
Classify based on fold types
F1
F2
F3
F4
----
Fn
SAP
*
Cluster: rmsd- ≤2 Å &
----
PID ≥60% cut-off
Average C coordinate of models of each cluster is used
to find the pair-wise distance between residues.
* Taylor (1999). Prot. Sci. 8:654-665.
Model score
 Pair-wise distance and product of CRESCENDO
scores between each pair of residues (that are at
least 8 residues apart in the linear sequence) are
calculated.
 The number (in %) of pair of residues that are within
the spatial distance of 12 Å, in the top 40 pairs
(based on product of CRESCENDO scores) was
calculated.
 The percentage scores were added in each step (in
steps of 5 pairs) to get the final score of the models.
Good and poor models of same fold type
2trxA- 34 clusters (with ≤ 2Å rmsd and ≥ 60% PID) were
obtained from 81 correct models
Why clustering between models of same type needed?
Function site prediction differs between models of same type due to
a) difference in loop conformation,
b) beta strand or helix shift even by a single residues.
So, even correct folds might have poor models (based on site
prediction).
3chy
H1
H5
C-term
1
S5
2
N-term
S2
H2
S3
S1
H3
S4
H4
Helix and strand order: H1(1,5);S2(2,1,3,4,5);H3(2,3,4)
3
Proximity plot:3chy
Best model in each foldtype
native
Correct model
Decoy fold distribution for 3chy
Fold
type
Strand and helix order
No. of
models in
each fold
type in
200
models
No. of
cluster
with ≤ 2Å
rmsd;
≥60% PID
cut-off
Score
of the
best
model
native
H1(1,5);S2(2,1,3,4,5);H3(2,3,4)
-
-
330.96
F1
H1(1,5);S2(2,1,3,4,5);H3(2,3,4)
161
61
314.76
F2
H1(1*,5);S2(2,1*,3,4,5);H3(2,3,4)
3
2
202.21
F3
H1(1,5);S2(2,3,1,4,5);H3(2,3,4)
16
11
145.19
F4
H1(1,3*,4*);S2(2,1,3*,4*,5*);H3(2*,5*)
2
2
150.83
F5
H1(1,4);S2(2,3,1,4,5*);H3(2,3,5*)
1
1
108.62
F6
H1(1,3,5);S2(2,1,4,3,5);H3(2,4)
11
7
250.20
F7
H1(1,5);S2(2,1,3,4,5);H3(2,3,4)
5
4
260.29
F8
H1(1,5*);S2(2,1,3,4,5);H3(2,3,4)
1
1
67.24
Summary plot: 3chy
Top 4 ranking models – rmsd (PID %)
PDB (length)
Rank-1
Rank-2
Rank-3
Rank-4
3chy (128)
3.6 (70.6)
3.8 (63.0)
4.8 (65.6)
8.83 (21.8)
1cozA (126)
6.5 (63.1)
9.0 (77.3)
14.2 (61.3)
7.9 (80.3)
2trxA (108)
4.7 (100.0)
12.9 (77.6)
14.1 (100.0)
5.6 (100.0)
1f4pA (148)
5.9 (80.1)
5.3 (82.9)
5.8 (100.0)
14.6 (100.0)
1di0A (147)
4.6 (82.5)
16.2 (71.4)
16.1 (96.5)
5.8 (53.6)
Top 4 ranking models – rmsd (PID %)
PDB (length)
Rank-1
Rank-2
Rank-3
Rank-4
1v9w (130)
13.4 (100.0)
11.3 (76.2)
6.9 (77.7)
6.3 (100.0)
1rlj (135)
13.7 (94.8)
4.9 (88.0)
11.2 (94.3)
13.7 (100.0)
1kjnA (159)
3.4 (26.3)
5.0 (59.7)
5.0 (62.6)
9.4 (5.8)
1vq1A (178)
8.5 (80.3)
9.5 (100.0)
7.1 (89.5)
7.9 (94.3)
1uxoA (186)
13.7 (90.7)
11.4 (100.0)
8.9 (100.0)
11.8 (94.6)
1t57A (186)
14.8 (32.0)
9.8 (77.2)
14.9 (96.2)
9.9 (92.2)
1vk2A (187)
16.4 (100.0)
14.7 (100.0)
14.5 (97.3)
15.9 (94.2)
Thioredoxin: 2trxA
correct
incorrect
incorrect
H5
Rank 1
Rank 4
Rank 10 (last)
Conclusions
• The requirement of proteins to form functional sites - used to
select the correct protein fold.
• In larger proteins, difficult due to the conformation of longer
loop
• The competing incorrect folds - mostly strand swapped models.
• Discriminates between incorrect fold and correct efficiently
when the direction of secondary structure element that contain
functional residues is altered and when the fold is messy.
Thanks to
• Dr Willie Taylor
National Institute for Medical Research,
Mill Hill,
London, UK.
• Prof Sir Tom Blundell
Department of Biochemistry,
University of Cambridge,
Cambridge, UK.