title: 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: 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 .
authors: EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118, Heidelberg, Germany. bingding.huang@eml-r.villa-bosch.de
authors: Bingding
authors: B
authors: Huang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D008024:Ligands; D011506:Proteins