Metapocket: A meta approach to improve protein ligand binding site prediction

Abstract

The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. Many computational methods for the prediction of ligand-binding sites have been developed in recent decades. Here we present a consensus method metaPocket, in which the predicted sites from four methods: LIGSITEcs, PASS, Q-SiteFinder, and SURFNET are combined together to improve the prediction success rate. All these methods are evaluated on two datasets of 48 unbound/bound structures and 210 bound structures. The comparison results show that metaPocket improves the success rate from ∼70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org. © 2009, Mary Ann Liebert, Inc.