An efficient algorithm for matching protein binding sites for protein function prediction

Comparing the binding sites of proteins is effective for predicting protein functions based on their structure information. However, it is still very challenging to predict the binding ligands from the atomic structures of protein binding sites. In this study, we designed a new algorithm based on the iterative closest point (ICP) algorithm. Our algorithm aims to find the maximum number of atoms that can be superposed between two protein binding sites, where any pair of matched superposed atoms has a distance smaller than a given threshold. The search starts from similar tetrahedra between two binding sites obtained from 3D Delaunay triangulation and uses the Hungarian algorithm to find additional matched atoms. We show that our method finds more matched atoms than a leading method. For benchmark data, we use the Tanimoto Index as a similarity measure and the nearest neighbor classifier to achieve a classification performance comparable to the best methods in the literature among those that provide both the common atom set and atom correspondences.