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Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set
Pei, Fen1; Jin, Hongwei1; Zhou, Xin1; Xia, Jie1,2; Sun, Lidan3; Liu, Zhenming1; Zhang, Liangren1
关键词Docking Knowledge-based Pharmacophore Mubd-decoymaker Shape-based 3d Similarity Search Tlr8 Agonists Virtual Screening
刊名CHEMICAL BIOLOGY & DRUG DESIGN
2015-11-01
DOI10.1111/cbdd.12590
86期:5页:1226-1241
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Biochemistry & Molecular Biology ; Chemistry, Medicinal
研究领域[WOS]Biochemistry & Molecular Biology ; Pharmacology & Pharmacy
关键词[WOS]MOLECULAR DOCKING ; DRUG DISCOVERY ; IN-SILICO ; SCORING FUNCTIONS ; IMMUNE-RESPONSES ; ACCURATE DOCKING ; LIGAND ; RECOGNITION ; INHIBITORS ; SHAPE
英文摘要

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed ′MUBD-DECOYMAKER′ protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists.

语种英语
WOS记录号WOS:000365404100028
项目编号2012AA020308 ; 81373272 ; 21272017
资助机构National High Technology Research and Development Program (&prime ; 863&prime ; ) of China ; National Natural Science Foundation of China
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/57952
专题北京大学药学院
北京大学药学院_天然药物与仿生药物国家重点实验室
北京大学药学院_药物化学系
北京大学第三临床医学院_口腔科
作者单位1.Peking Univ, Sch Pharmaceut Sci, State Key Lab Nat & Biomimet Drugs, Beijing 100191, Peoples R China
2.Howard Univ, Mol Modeling & Drug Discovery Core Dist Columbia, Lab Cheminfomat & Drug Design, Dept Pharmaceut Sci,Coll Pharm, Washington, DC 20059 USA
3.China Three Gorges Univ, Sch Med, Inst Mol Biol, Yichang 443002, Peoples R China
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GB/T 7714
Pei, Fen,Jin, Hongwei,Zhou, Xin,et al. Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set[J]. CHEMICAL BIOLOGY & DRUG DESIGN,2015,86(5):1226-1241.
APA Pei, Fen.,Jin, Hongwei.,Zhou, Xin.,Xia, Jie.,Sun, Lidan.,...&Zhang, Liangren.(2015).Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set.CHEMICAL BIOLOGY & DRUG DESIGN,86(5),1226-1241.
MLA Pei, Fen,et al."Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set".CHEMICAL BIOLOGY & DRUG DESIGN 86.5(2015):1226-1241.
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