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CovBinderInPDB:一个基于结构的共价配体数据库。

CovBinderInPDB: A Structure-Based Covalent Binder Database.

机构信息

Department of Chemistry, New York University, New York, New York 10003, United States.

Simons Center for Computational Physical Chemistry, New York University, New York, New York 10003, United States.

出版信息

J Chem Inf Model. 2022 Dec 12;62(23):6057-6068. doi: 10.1021/acs.jcim.2c01216. Epub 2022 Dec 1.

Abstract

Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.

摘要

共价抑制已成为一种有前途的药物发现正交方法,尽管实现靶标特异性具有重大挑战。为了促进基于结构的靶向共价调节剂的合理设计,我们开发了一种集成的计算方案,从 RCSB 蛋白质数据库(PDB)中提取共价结合物。从 PDB 档案中的大分子晶体学信息文件(mmCIF)开始,收集并清理了表明氨基酸残基通过共价结合物发生侧链修饰的共价键记录。然后,借助 PDB 中的化学组分词典,恢复靶向残基与共价结合物之间化学反应的产物——残基-结合物加合物。最后,采用了几种策略从加合物中提取共价结合物的预反应形式。我们整理的 CovBinderInPDB 数据库包含 7375 种共价修饰,其中 2189 种独特的共价结合物靶向 9 种类型的氨基酸残基(半胱氨酸、赖氨酸、丝氨酸、天冬氨酸、谷氨酸、组氨酸、甲硫氨酸、苏氨酸和酪氨酸),来自 3555 个包含 1170 种独特蛋白质链的复合物结构。该数据库将为开发和基准测试共价调节剂设计的计算策略奠定坚实基础,并可在 https://yzhang.hpc.nyu.edu/CovBinderInPDB 上免费获取。

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本文引用的文献

1
CovPDB: a high-resolution coverage of the covalent protein-ligand interactome.
Nucleic Acids Res. 2022 Jan 7;50(D1):D445-D450. doi: 10.1093/nar/gkab868.
2
Covalent inhibitors: a rational approach to drug discovery.
RSC Med Chem. 2020 Jul 2;11(8):876-884. doi: 10.1039/d0md00154f. eCollection 2020 Aug 1.
3
Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors.
Trends Pharmacol Sci. 2020 Dec;41(12):1038-1049. doi: 10.1016/j.tips.2020.10.005. Epub 2020 Nov 2.
4
CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.
Nucleic Acids Res. 2021 Jan 8;49(D1):D1122-D1129. doi: 10.1093/nar/gkaa876.
5
Covalent fragment libraries in drug discovery.
Drug Discov Today. 2020 Jun;25(6):983-996. doi: 10.1016/j.drudis.2020.03.016. Epub 2020 Apr 13.
7
Advances in covalent kinase inhibitors.
Chem Soc Rev. 2020 May 7;49(9):2617-2687. doi: 10.1039/c9cs00720b. Epub 2020 Mar 30.
8
Recent Advances in Selective and Irreversible Covalent Ligand Development and Validation.
Cell Chem Biol. 2019 Nov 21;26(11):1486-1500. doi: 10.1016/j.chembiol.2019.09.012. Epub 2019 Oct 17.
9
New Electrophiles and Strategies for Mechanism-Based and Targeted Covalent Inhibitor Design.
Biochemistry. 2019 Dec 31;58(52):5234-5244. doi: 10.1021/acs.biochem.9b00293. Epub 2019 Apr 24.
10
Covalent binders in drug discovery.
Prog Med Chem. 2019;58:1-62. doi: 10.1016/bs.pmch.2018.12.002. Epub 2019 Mar 11.

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