Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues

Accurate prediction of the structure of proteinprotein complexes in computational docking experiments remains a formidable challenge. It has been recognized that identifying native or native-like poses among multiple decoys is the major bottleneck of the current scoring functions used in docking. We have developed a novel multibody pose-scoring function that has no theoretical limit on the number of residues contributing to the individual interaction terms. We use a coarse-grain representation of a proteinprotein complex where each residue is represented by its side chain centroid. We apply a computational geometry approach called Almost-Delaunay tessellation that transforms proteinprotein complexes into a residue contact network, or an undirectional graph where vertex-residues are nodes connected by edges. This treatment forms a family of interfacial graphs representing a dataset of proteinprotein complexes. We then employ frequent subgraph mining approach to identify common interfacial residue patterns that appear in at least a subset of native proteinprotein interfaces. The geometrical parameters and frequency of occurrence of each native pattern in the training set are used to develop the new SPIDER scoring function. SPIDER was validated using standard ZDOCK benchmark dataset that was not used in the development of SPIDER. We demonstrate that SPIDER scoring function ranks native and native-like poses above geometrical decoys and that it exceeds in performance a popular ZRANK scoring function. SPIDER was ranked among the top scoring functions in a recent round of CAPRI (Critical Assessment of PRedicted Interactions) blind test of proteinprotein docking methods. Proteins 2012; (c) 2012 Wiley Periodicals, Inc.