title: Improved prediction of protein ligand-binding sites using random forests.
abstract: This article describes a novel method for predicting ligand-binding sites of proteins. This method uses only 8 structural properties as input vector to train 9 random forest classifiers which are combined to predict binding residues. These predicted binding residues are then clustered into some predicted ligand-binding sites. According to our measurement criterion, this method achieved a success rate of 0.914 in the bound state dataset and 0.800 in the unbound state dataset, which are better than three other methods: Q-SiteFinder, SCREEN and Morita's method. It indicates that the proposed method here is successful for predicting ligand-binding sites.
authors.affiliation: The State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology,2 Ling-Gong Road, Dalian 116024, China. guixum@dlut.edu.cn
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Qiu
authors.forename: Xicheng
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins