Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-11-28 DOI:10.1021/acs.jcim.4c01594
Muya Xiong, Tianqing Nie, Zhewen Li, Meiyi Hu, Haixia Su, Hangchen Hu, Yechun Xu, Qiang Shao
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Abstract

3-Chymotrypsin-like protease (3CLpro) is a prominent target against pathogenic coronaviruses. Expert knowledge of the cysteine-targeted covalent reaction mechanism is crucial to predict the inhibitory potency of approved inhibitors against 3CLpros of SARS-CoV-2 variants and perform structure-based drug design against newly emerging coronaviruses. We carried out an extensive array of classical and hybrid QM/MM molecular dynamics simulations to explore covalent inhibition mechanisms of five well-characterized inhibitors toward SARS-CoV-2 3CLpro and its mutants. The calculated binding affinity and reactivity of the inhibitors are highly consistent with experimental data, and the predicted inhibitory potency of the inhibitors against 3CLpro with L167F, E166V, or T21I/E166V mutant is in full agreement with IC50s determined by the accompanying enzymatic assays. The explored mechanisms unveil the impact of residue mutagenesis on structural dynamics that communicates to change not only noncovalent binding strength but also covalent reaction free energy. Such a change is inhibitor dependent, corresponding to varied levels of drug resistance of these 3CLpro mutants against nirmatrelvir and simnotrelvir and no resistance to the 11a compound. These results together suggest that the present simulations with a suitable protocol can efficiently evaluate the reactivity and potency of covalent inhibitors along with the elucidated molecular mechanisms of covalent inhibition.

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通过多尺度模拟预测针对 SARS-CoV-2 3CL 样蛋白酶和多种突变体的共价抑制剂的效力
3-胰蛋白酶样蛋白酶(3CLpro)是抗击致病性冠状病毒的一个重要靶点。半胱氨酸靶向共价反应机制的专业知识对于预测已批准的抑制剂对 SARS-CoV-2 变体的 3CLpros 的抑制效力以及针对新出现的冠状病毒进行基于结构的药物设计至关重要。我们进行了大量经典和混合 QM/MM 分子动力学模拟,以探索五种表征良好的抑制剂对 SARS-CoV-2 3CLpro 及其变体的共价抑制机制。计算出的抑制剂的结合亲和力和反应性与实验数据高度一致,预测的抑制剂对带有 L167F、E166V 或 T21I/E166V 突变体的 3CLpro 的抑制效力与相应的酶学实验测定的 IC50 完全一致。所探讨的机制揭示了残基突变对结构动态的影响,这种影响不仅改变了非共价结合强度,还改变了共价反应自由能。这种变化依赖于抑制剂,因此这些 3CLpro 突变体对 nirmatrelvir 和 simnotrelvir 具有不同程度的耐药性,而对 11a 化合物则没有耐药性。这些结果表明,采用合适的方案进行模拟,可以有效地评估共价抑制剂的反应性和效力,并阐明共价抑制的分子机制。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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