title: An accurate method for prediction of protein-ligand binding site on protein surface using SVM and statistical depth function.
abstract: Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery. In the past two decades, a lot of algorithms were present to predict the protein-ligand binding site. In this paper, we introduce statistical depth function to define negative samples and propose an SVM-based method which integrates sequence and structural information to predict binding site. The results show that the present method performs better than the existent ones. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively; on the independent test set, the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively.
authors.affiliation: College of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
authors.forename: Kui
authors.initials: K
authors.lastname: Wang
authors.forename: Jianzhao
authors.initials: J
authors.lastname: Gao
authors.forename: Shiyi
authors.initials: S
authors.lastname: Shen
authors.forename: Jack A
authors.initials: JA
authors.lastname: Tuszynski
authors.forename: Jishou
authors.initials: J
authors.lastname: Ruan
authors.forename: Gang
authors.initials: G
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D008024:Ligands; D008565:Membrane Proteins / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software; D013499:Surface Properties
chemical_list: D008024:Ligands; D008565:Membrane Proteins; D011506:Proteins