1. Discrimination of near-native structures by clustering docked conformations and the selection of the optimal radius D. Kozakov 1, K. H. Clodfelter 2, C. J. Camacho 1,3, and S. Vajda 1,2 1 Department of Biomedical Engineering 2 Program in Bioinformatics, Boston University 3 Current address: University of Pittsburgh

2. 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

3. 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

4. How to analyze clustering properties of distribution?

5. How to describe clustering property? ● Δ characterize intra- to inter- cluster elements ratio ● Δ=1 Data set well separated ● Δ=0 No clustering ● Δ>Δ n Distribution carries cluster size information ● Optimal Radius (OR): First minimum with the largest Δ

6. 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.

7. 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

8. How does docking histograms look like? OR measure – property of sampled energy landscape

9. 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 EnergyClustering Top 15%38% Top 1014%74% Top 3019%93% Top 5031%100%

10. Fixed radius prediction compared to optimal + - Complex9 Å rankOR Rank Δ 2PCC42480.745 1MEL210.7 1ATN210.617 1STF110.615 1UDI1010.587 1AVW110.587 2TEC110.563 2BTF730.561 2PTC330.52 2KAI2580.514 1QFU39110.492 1UGH510.489 1BRS15160.441 1MDA13120.431 2SIC210.423 1BQL630.406 Complex9 Å rankOR Rank Δ 1AHW120.389 1CHO110.384 1WQ1130.383 1IAI15220.381 1TAB1180.364 4HTC310.346 1NCA120.343 1NMB1060.311 1BVK4110.304 2SNI1170.302 1CSE920.286 1MLC1420.243 1SPB110.208 1DQJ26370.206 1FBI17320.138 2JEL6130.108 1ACB310.102

11. Can we see the clusters?

12. Acknowledgments ● Sandor Vajda ● Carlos Camacho