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

3-Chymotrypsin-like protease (3CL<sup>pro</sup>) 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 3CL<sup>pro</sup>s 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 3CL<sup>pro</sup> 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 3CL<sup>pro</sup> with L167F, E166V, or T21I/E166V mutant is in full agreement with IC<sub>50</sub>s 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 3CL<sup>pro</sup> mutants against nirmatrelvir and simnotrelvir and no resistance to the <b>11a</b> 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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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-11-28
DOI
https://doi.org/10.1021/acs.jcim.4c01594
Citations
6
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
2.17

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article

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

Muya Xiong, Yechun Xu, Hangchen Hu, Tianqing Nie et al.
6 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
2.17
article

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

Muya Xiong, Yechun Xu, Hangchen Hu, Tianqing Nie, Haixia Su, Zhewen Li, Qiang Shao, Miao Hu
article en
6 citations

Abstract

3-Chymotrypsin-like protease (3CL<sup>pro</sup>) 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 3CL<sup>pro</sup>s 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 3CL<sup>pro</sup> 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 3CL<sup>pro</sup> with L167F, E166V, or T21I/E166V mutant is in full agreement with IC<sub>50</sub>s 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 3CL<sup>pro</sup> mutants against nirmatrelvir and simnotrelvir and no resistance to the <b>11a</b> 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.

Journal of Chemical Information and ModelingVol. 64(24)
Chinese Academy of Sciences (CN), ShanghaiTech University (CN), Shanghai Institute of Materia Medica (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 11%
Computational Drug Discovery Methods
2.17
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