title: Predicting protein-ligand interactions based on chemical preference features with its application to new D-amino acid oxidase inhibitor discovery.
abstract: In silico prediction of the new drug-target interactions from existing databases is of important value for the drug discovery process. Currently, the amount of protein targets that have been identified experimentally is still very small compared with the entire human proteins. In order to predict protein-ligand interactions in an accurate manner, we have developed a support vector machine (SVM) model based on the chemical-protein interactions from STITCH. New features from ligand chemical space and interaction networks have been selected and encoded as the feature vectors for SVM analysis. Both the 5-fold cross validation and independent test show high predictive accuracy that outperforms the state-of-the-art method based on ligand similarity. Moreover, 91 distinct pairs of features have been selected to rebuild a simplifier model, which still maintains the same performance as that based on all 332 features. Then, this refined model is used to search for the potential D-amino acid oxidase inhibitors from STITCH database and the predicted results are finally validated by our wet experiments. Out of 10 candidates obtained, seven D-amino acid oxidase inhibitors have been verified, in which four are newly found in the present study, and one may have a new application in therapy of psychiatric disorders other than being an antineoplastic agent. Clearly, our model is capable of predicting potential new drugs or targets on a large scale with high efficiency.
authors.forename: Mingzhu
authors.initials: M
authors.lastname: Zhao
authors.forename: Hao-Teng
authors.initials: HT
authors.lastname: Chang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhou
authors.forename: Tao
authors.initials: T
authors.lastname: Zeng
authors.forename: Chung-Shiuan
authors.initials: CS
authors.lastname: Shih
authors.forename: Zhi-Ping
authors.initials: ZP
authors.lastname: Liu
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Microbial Metabolism and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. zpliu@sibs.ac.cn.
authors.forename: Dong-Qing
authors.initials: DQ
authors.lastname: Wei
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D003198:Computer Simulation; D003605:D-Amino-Acid Oxidase / Q000037:antagonists & inhibitors*; D016208:Databases, Factual; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods; D006801:Humans; D008024:Ligands; D001523:Mental Disorders / Q000188:drug therapy; D008962:Models, Theoretical; D058990:Molecular Targeted Therapy; D011506:Proteins / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D000970:Antineoplastic Agents; D008024:Ligands; D011506:Proteins; D003605:D-Amino-Acid Oxidase