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Functional Site Prediction Selects Correct Protein Models. Vijayalakshmi Chelliah vchelli@nimr.mrc.ac.uk Division of Mathematical Biology National Institute for Medical Research Mill Hill, London. Sixth International Conference on Bioinformatics InCoB2007 HKUST, Hong Kong

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Functional Site Prediction Selects Correct Protein Models


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functional site prediction selects correct protein models

Functional Site Prediction Selects Correct Protein Models

Vijayalakshmi Chelliah

vchelli@nimr.mrc.ac.uk

Division of Mathematical Biology

National Institute for Medical Research

Mill Hill, London

Sixth International Conference on Bioinformatics InCoB2007HKUST, Hong Kong

27th – 30th August 2007

slide2
Functional site prediction - applications:
  • To predict function of the protein (Pazos & sternberg, 2004; PNAS 101:14754-9)
  • In protein – protein docking: To select the near-native 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)
slide3

Gene sequence

Protein sequence

Predict structure:

De-novo/ab-initio

Xray/NMR

Protein structure

Protein structure

select correct models

Functional site prediction

overview
Overview
  • De-novo protein structure prediction method (decoy generation)
  • Functional site prediction method
  • Evaluating models
  • Conclusions
de novo protein structure prediction method
De-novo protein structure prediction method

SEQUENCE ALIGNMENT

IDEAL FORMS

Predicted Res. burial

Predicted sec. structure

Fold Generation

and scoring

*Taylor (2002). Nature. 416:657-660

Secondary structure

‘stick’ level

Top 1/3

C models

Threading

Top 100+N

Residue level

Refinement

STRUCTURE PATTERNS

Top 100+N

Main-chain level

Top 200 models

slide6

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.
slide7

CRESCENDO: Functional site prediction method

*

Environment specific substitution table

Alignment position 1 2 3 4 5 6………………..

(sp1+sp2+sp3+sp+…+spN)/N = Expected substitution pattern for each amino acid (q) at tth position

sp1

sp2

sp3

sp4

sp-

sp-

spN

Multiple sequence alignment of the homologous sequences: structure based sequence alignment

Observed substitution pattern for each amino acid (p) at tth position

Divergent score between the observed (p) and expected (q) substitution table

  • *Overington et al., (1992). Protein Science 1:216-26
assumptions
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?
slide9

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
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
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).

slide12

3chy

1

C-term

H1

H5

N-term

S4

2

S3

S2

S1

S5

3

H2

H4

H3

Helix and strand order: H1(1,5);S2(2,1,3,4,5);H3(2,3,4)

slide13

Proximity plot:3chy

Best model in each foldtype

native

Correct model

slide18

Thioredoxin: 2trxA

correct

incorrect

incorrect

H5

Rank 1

Rank 4

Rank 10 (last)

conclusions
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
Thanksto
  • Dr Willie Taylor

National Institute for Medical Research,

Mill Hill,

London, UK.

  • Prof Sir Tom Blundell

Department of Biochemistry,

University of Cambridge,

Cambridge, UK.