discrimination of near-native structures by clustering docked conformations and the selection of the...
TRANSCRIPT
Discrimination of near-native structures by clustering docked
conformations and the selection of the optimal radius
D. Kozakov1, K. H. Clodfelter2, C. J. Camacho1,3,
and S. Vajda1,2
1Department of Biomedical Engineering
2Program in Bioinformatics, Boston University
3Current address: University of Pittsburgh
Why do we need clustering?
● Rigid body docking methods sample a large set of conformations which uniformly cover the energy landscape
● Energy scoring functions are not enough to discriminate between near native structures
● unbound crystal structure conformations are not the same as when in solvent– difficulty in estimating the solvation effects
● Distribution of sampled conformations in such cases has more information than single conformations alone
What clustering means for docking?
● Low energy conformations below a given threshold will cluster
● Clusters are representative of the energy minima
● The cluster in the native funnel should be the most populated
How to analyze clustering propertiesof distribution?
How to describe clustering property?
● Δ characterize intra- to inter- cluster elements ratio
● Δ=1 Data set well separated
● Δ=0 No clustering
● Δ>Δn Distribution carries cluster size information
● Optimal Radius (OR): First minimum with the largest Δ
Clustering Procedure● Element with maximum number of
neighbors is chosen. It is called the cluster centre.
● All the elements within the optimal radius are included in the cluster.
● Exclude these elements and repeat until all points are exhausted.
● Redistribute the elements to their closest cluster centre.
● Rank the clusters based on size.
● Clusters with a size less than 10 are ignored.
Application to Docking
● Rigid body methods uniformly sample the placement of the ligand around a fixed receptor
● Best conformations are chosen based on shape complementarities and a simple energy scoring
● The total set of conformations considered is 2000-20,000 in size
● We choose N of the lowest energy desolvation (ACP) conformations and 3N of the lowest electrostatic energy conformations (N = 50-500)
● A distance of 6-9 Å is the characteristic size of attractors from these potentials
How does docking histograms look like?
•OR measure – property of sampled energy landscape
Results● Tested on the benchmark set of
protein complexes
● Hit is rank of first best cluster with center within a distance of 10 Å RMSD from native bound conformation
● “Biggest cluster = native funnel” is supported
● Clusters – starting points for further refinement
Successful
Prediction
Ranking based on
Free Energy Clustering
Top 1 5% 38%
Top 10 14% 74%
Top 30 19% 93%
Top 50 31% 100%
Fixed radius prediction compared to optimal
+ -Complex 9 Å rank OR Rank Δ
2PCC 42 48 0.745
1MEL 2 1 0.7
1ATN 2 1 0.617
1STF 1 1 0.615
1UDI 10 1 0.587
1AVW 1 1 0.587
2TEC 1 1 0.563
2BTF 7 3 0.561
2PTC 3 3 0.52
2KAI 25 8 0.514
1QFU 39 11 0.492
1UGH 5 1 0.489
1BRS 15 16 0.441
1MDA 13 12 0.431
2SIC 2 1 0.423
1BQL 6 3 0.406
Complex 9 Å rank OR Rank Δ
1AHW 1 2 0.389
1CHO 1 1 0.384
1WQ1 1 3 0.383
1IAI 15 22 0.381
1TAB 11 8 0.364
4HTC 3 1 0.346
1NCA 1 2 0.343
1NMB 10 6 0.311
1BVK 4 11 0.304
2SNI 11 7 0.302
1CSE 9 2 0.286
1MLC 14 2 0.243
1SPB 1 1 0.208
1DQJ 26 37 0.206
1FBI 17 32 0.138
2JEL 6 13 0.108
1ACB 3 1 0.102
Can we see the clusters?
Acknowledgments
● Sandor Vajda● Carlos Camacho