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作 者:Liu Yang Wei He Yuehui Yun Yongxiang Gao Zhongliang Zhu Maikun Teng Zhi Liang Liwen Niu
机构地区:[1]School of Life Sciences,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230026,China [2]Division of Molecular and Cellular Biophysics,Hefei National Laboratory for Physical Sciences at the Microscale,Hefei 230026,China
出 处:《Genomics, Proteomics & Bioinformatics》2022年第4期765-779,共15页基因组蛋白质组与生物信息学报(英文版)
基 金:supported by the National Natural Science Foundation of China(Grant No.31621002);the Ministry of Science and Technology of China(Grant No.2017YFA0504903 to LN);the Hefei National Science Center Pilot Project Funds,China(in part)。
摘 要:Uncovering conserved 3D protein–ligand binding patterns on the basis of functional groups(FGs)shared by a variety of small molecules can greatly expand our knowledge of protein–ligand interactions.Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature,large-scale identification and evaluation of FG-based 3D binding motifs are still lacking.Here,we propose a computational method,Automatic FG-based Three-dimensional Motif Extractor(AFTME),for automatic mapping of 3D motifs to different FGs of a specific ligand.Applying our method to 233 naturally-occurring ligands,we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets.Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs.Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities.Finally,we show that our FG–motif map can be used to nominate FGs that potentially bind to specific drug targets,thus providing useful insights and guidance for rational design of small-molecule drugs.
关 键 词:Protein–ligand interaction Functional group Binding motif Computational method Drug design
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