Application of Virtual Screening and Molecular Modeling Technologies to Identify Potential SARS-CoV-2 Main Protease Inhibitors

Q3 Mathematics
A. Andrianov, K. V. Furs, A. V. Gonchar, L.H. Aslanyan, A. Tuzikov
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引用次数: 0

Abstract

A virtual screening of the molecular library of biologically active compounds was carried out to identify potential inhibitors of SARS-CoV-2 main protease (Mpro) which plays an important role in the process of virus replication. Using molecular docking and molecular dynamics, the binding energy of these compounds to the catalytic site of the enzyme was assessed, resulting in six molecules that exhibited high chemical affinity for SARS-CoV-2 Mpro. This is evidenced by the low values of the binding free energy of the ligand/Mpro complexes comparable with those predicted for the potent non-covalent SARS-CoV-2 Mpro inhibitor using the identical computational protocol. Based on the data obtained, it was concluded that the identified compounds have a good therapeutic potential for inhibiting the catalytic activity of the enzyme and form promising basic structures for the development of new effective drugs against COVID-19.
应用虚拟筛选和分子建模技术鉴定潜在的SARS-CoV-2主要蛋白酶抑制剂
对生物活性化合物分子文库进行虚拟筛选,以确定在病毒复制过程中起重要作用的SARS-CoV-2主蛋白酶(Mpro)的潜在抑制剂。利用分子对接和分子动力学方法,评估了这些化合物与酶催化位点的结合能,得到了6个对SARS-CoV-2 Mpro具有高化学亲和力的分子。配体/Mpro复合物的结合自由能值与使用相同计算方案预测的强效非共价SARS-CoV-2 Mpro抑制剂的结合自由能值相当,证明了这一点。基于所获得的数据,鉴定的化合物具有良好的抑制酶催化活性的治疗潜力,并为开发新的COVID-19有效药物形成了有希望的基本结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biology and Bioinformatics
Mathematical Biology and Bioinformatics Mathematics-Applied Mathematics
CiteScore
1.10
自引率
0.00%
发文量
13
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