Functional site prediction by exploiting correlations between labels of interacting residues

Abstract

Functional site prediction is an important problem in the structural genomics era where we have a large number of experimentally determined protein structures with unknown function. The functional sites provide useful insights into protein function. In this paper, we propose a method for prediction of functional residues in a given protein from its three-dimensional (3D) structure. Our method exploits correlation between labels of interacting residues to obtain significant performance improvements over the existing methods on the benchmark dataset. We represent each protein as a weighted undirected residue interaction network, where spatially proximal residues in terms of their Van der Waal radii are connected by an edge. The edge weight captures correlation between the labels of interacting residues. The correlation is estimated based on the features of interacting residues. We then obtain a label assignment by minimizing combined cost of residue-wise label misclassification and violation of label correlation constraints. We solve this problem in two stages, where the first stage minimizes residue-wise label misclassification cost followed by an iterative collective inference scheme that adjusts the labels predicted in the first stage so as to minimize the correlation constraint violations. Our approach significantly outperforms state of the art methods on standard benchmark dataset. It achieves 23.06% precision at 69% recall and 87.78% recall at 18% precision, which translates to an improvement of 5.06 percentage points in the precision at 69% recall and 18.78 percentage point improvement in recall at 18% precision.