MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction
- 1 August 2009
- journal article
- Published by Mary Ann Liebert Inc in OMICS: A Journal of Integrative Biology
- Vol. 13 (4), 325-330
- https://doi.org/10.1089/omi.2009.0045
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: LIGSITE(cs), 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 approximately 70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org .Keywords
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