TY - JOUR
T1 - PharmMapper server
T2 - A web server for potential drug target identification using pharmacophore mapping approach
AU - Liu, Xiaofeng
AU - Ouyang, Sisheng
AU - Yu, Biao
AU - Liu, Yabo
AU - Huang, Kai
AU - Gong, Jiayu
AU - Zheng, Siyuan
AU - Li, Zhihua
AU - Li, Honglin
AU - Jiang, Hualiang
N1 - Funding Information:
Major State Basic Research Project (grants 2009CB918501 and 2009CB918502); National Natural Science Foundation of China (grants 20803022 and 20721003); Shanghai Committee of Science and Technology (grants 09dZ1975700 and 08JC1407800); 863 Hi-Tech Program of China (grants 2007AA02Z304 and 2007AA02Z330); Major National Scientific and Technological Project of China (grants 2009ZX09501-001 and 2009ZX09301-001); 111 Project (grant B07023); Shanghai Rising-Star Program (grant 10QA1401800 to H.L.). Funding for open access charge: the 863 Hi-Tech Program of China (grant 2007AA02Z304).
PY - 2010/4/29
Y1 - 2010/4/29
N2 - In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule's aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper.
AB - In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule's aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper.
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U2 - 10.1093/nar/gkq300
DO - 10.1093/nar/gkq300
M3 - Article
C2 - 20430828
AN - SCOPUS:77954264038
SN - 0305-1048
VL - 38
SP - W609-W614
JO - Nucleic acids research
JF - Nucleic acids research
IS - SUPPL. 2
M1 - gkq300
ER -