title: Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set.
abstract: 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.
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
authors.forename: Fen
authors.initials: F
authors.lastname: Pei
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
authors.forename: Hongwei
authors.initials: H
authors.lastname: Jin
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhou
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.|Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminfomatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC, 20059, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Xia
authors.affiliation: The Institute of Molecular Biology, Medical School of China Three Gorges University, 8 Daxue Road, Yichang, 443002, China.
authors.forename: Lidan
authors.initials: L
authors.lastname: Sun
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
authors.forename: Zhenming
authors.initials: Z
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
authors.forename: Liangren
authors.initials: L
authors.lastname: Zhang
mesh_terms: D001665:Binding Sites; D017076:Computer-Aided Design; D015195:Drug Design; D006801:Humans; D062105:Molecular Docking Simulation; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000494:pharmacology*; D012984:Software; D051200:Toll-Like Receptor 8 / Q000819:agonists* / Q000737:chemistry / Q000378:metabolism*
chemical_list: D054852:Small Molecule Libraries; C495349:TLR8 protein, human; D051200:Toll-Like Receptor 8
keywords: MUBD-DecoyMaker; TLR8 agonists; docking; knowledge-based pharmacophore; shape-based 3D similarity search; virtual screening