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基于对接的 SARS-CoV-2 主蛋白酶抑制剂虚拟筛选的组合。

Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors.

机构信息

FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia.

Department of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia.

出版信息

Mol Inform. 2024 Aug;43(8):e202300279. doi: 10.1002/minf.202300279. Epub 2024 Jul 8.

DOI:10.1002/minf.202300279
PMID:38973780
Abstract

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

摘要

在 COVID-19 大流行的最初几年,以空前的速度获得了冠状病毒药物靶点的 X 射线结构,在不到一年的时间里就有数百个 PDB 沉积物。严重急性呼吸系统综合征相关冠状病毒 2 (SARS-CoV-2) 的主要蛋白酶 (Mpro) 是直接作用抗病毒药物的主要验证靶点。因此,用于对接驱动虚拟筛选的 Mpro 的最佳结构组合的选择并非微不足道,需要一种系统和自动化的方法。在这里,我们报告了一种基于半自动化活性位点均方根偏差的 SARS-CoV-2 Mpro 晶体学数据的组合选择程序,以及对其抑制剂的虚拟筛选。该程序与其他组合选择方法进行了比较,并通过手工挑选和同行评审的活性注释库进行了验证。对非共价 Mpro 抑制剂的前瞻性虚拟筛选产生了一种新的噻吩并嘧啶酮衍生物的化学型,其酶抑制作用已通过实验证实。

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