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

doi:10.1021/acs.jcim.4c01594

	Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations
	Muya Xiong 1,2; Tianqing Nie3,4 ; Zhewen Li 2,5; Meiyi Hu 2; Haixia Su 2,5; Hangchen Hu 2,5; Yechun Xu 1,2,5; Qiang Shao 2,5
	2024-11-01
发表期刊	JOURNAL OF CHEMICAL INFORMATION AND MODELING
ISSN	1549-9596
EISSN	1549-960X
卷号	64 期号:24
发表状态	已发表
DOI	10.1021/acs.jcim.4c01594
摘要	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.
关键词	Binding energy Molecular docking Structural dynamics Coronaviruses Covalent reaction Dynamics simulation Expert knowledge Hybrid QM/MM Inhibition mechanisms Multi-scale simulation Pathogenics Reaction mechanism Structure based drug designs
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收录类别	EI ; SCI
语种	英语
资助项目	National Natural Science Foundation of China["32071248","22277130","22307133","32301050"]
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
WOS类目	Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001366395100001
出版者	AMER CHEMICAL SOC
EI入藏号	20244817447066
EI主题词	Coronavirus
EI分类号	102.1.2 ; 1106.1 ; 408 Structural Design ; 801.3 Colloid Chemistry
原始文献类型	Article in Press
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/452316
专题	物质科学与技术学院物质科学与技术学院_博士生
共同第一作者	Tianqing Nie
通讯作者	Yechun Xu; Qiang Shao
作者单位	1.School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China. 2.State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. 3.Lingang Laboratory, Shanghai 200031, China. 4.School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China. 5.University of Chinese Academy of Sciences, Beijing 100049, China.
推荐引用方式 GB/T 7714	Muya Xiong,Tianqing Nie,Zhewen Li,et al. Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2024,64(24).
APA	Muya Xiong.,Tianqing Nie.,Zhewen Li.,Meiyi Hu.,Haixia Su.,...&Qiang Shao.(2024).Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations.JOURNAL OF CHEMICAL INFORMATION AND MODELING,64(24).
MLA	Muya Xiong,et al."Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations".JOURNAL OF CHEMICAL INFORMATION AND MODELING 64.24(2024).