Clustering of low energy docked conformations

We cluster the retained 1000 conformations using pairwise ligand RMSD as the distance measure. Our goal is finding large clusters of structures below a certain energy level, indicating minima that are both deep and have a broad region of attraction. We use a simple greedy algorithm to find the structures with the largest number of neighbors within a clustering radius rc. The value of r_c depends on a clustering parameter 0 ≤ Δ ≤ 1, which is based on the histogram of pairwise RMSD values, and measures the depth of the separation between clusters (Kozakov et al., 2005). We generally retain 30 clusters, each of them indicating a region of attraction around a local energy minimum.

We have recently reduced the number of retained clusters by testing the stability of local minima (Kozakov and Vajda, 2008). Since structures at narrow minima loose more entropy, some of the non-native states can be detected by determining whether or not a local minimum is surrounded by a broad region of attraction on the energy surface. The analysis is based on starting Monte Carlo Minimization (MCM) runs from random points around each minimum, and observing whether a certain fraction of trajectories converge to a small region within the cluster. The cluster is considered stable if such a strong attractor exists, has at least 10 convergent trajectories, is relatively close to the original cluster center, and contains a low energy structure. We studied the stability of clusters for enzyme-inhibitor and antibody-antigen complexes. All clusters that are close to the native structure are stable. Restricting considerations to stable clusters eliminates around half of the false positives, i.e., solutions that are low in energy but far from the native structure of the complex.