Discrimination of near-native structures by clustering docked conformations and the selection of the optimal radius

1. 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 Discrimination of near-native structures by clustering docked conformations and the selection of the optimal radius

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 propertiesof 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>Δn 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

10. Fixed radius prediction compared to optimal+ -

11. Can we see the clusters?

12. Acknowledgments • Sandor Vajda • Carlos Camacho