Dissecting the Molecular Mechanism Concerning Conformational Changes of SARS‐CoV‐2 Main Protease Mediated by Binding of Natural Phytochemicals via Molecular Dynamics Simulations

Abstract The novel SARS‐CoV‐2 outbreak has swept the world since 2019. Because of the lack of direct‐acting antivirals (DAAs) targeting SARS‐CoV‐2 as well as the side effects of synthetic drugs, naturally sourced DAAs targeting SARS‐CoV‐2 should be urgently developed. The SARS‐CoV‐2 main protease (M pro ) can be reasonably used as a drug target because it is crucially involved in viral replication and transcription. To design potent DAAs targeting M pro , the M pro ‐inhibitor binding mechanism, and the conformational changes of M pro induced by inhibitor binding must be essentially clarified. Here, conventional molecular dynamics (cMD) simulation, principal component analysis (PCA), and free energy landscape (FEL) analysis were combined to probe 5 known phytochemical inhibitors‐mediated conformational changes of M pro . The results revealed that inhibitor binding significantly affected the kinetic behavior of M pro , and induced conformational rearrangement. The calculations of binding free energies using molecular mechanics generalized Born surface area (MM‐GBSA) and solvation interaction energy (SIE) methods and residue‐based free energy decomposition values showed that there are 23 key residues involved in the M pro ‐inhibitor binding. We found that one of the tested phytochemicals (PubChem CID 637112) could be used as a potent SARS‐CoV‐2 M pro inhibitor, which can block viral replication and translation.

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Publication Details

Journal
ChemistrySelect
Published
2025-04-01
DOI
https://doi.org/10.1002/slct.202405868
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01

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article

Dissecting the Molecular Mechanism Concerning Conformational Changes of SARS‐CoV‐2 Main Protease Mediated by Binding of Natural Phytochemicals via Molecular Dynamics Simulations

Xinguo Liu, Yanqi Sun, Jianzhong Chen, Chaoyue Jia
2 citations
ChemistrySelect
Computational Drug Discovery Methods
3.01
article

Dissecting the Molecular Mechanism Concerning Conformational Changes of SARS‐CoV‐2 Main Protease Mediated by Binding of Natural Phytochemicals via Molecular Dynamics Simulations

Xinguo Liu, Yanqi Sun, Jianzhong Chen, Chaoyue Jia
article en
2 citations

Abstract

Abstract The novel SARS‐CoV‐2 outbreak has swept the world since 2019. Because of the lack of direct‐acting antivirals (DAAs) targeting SARS‐CoV‐2 as well as the side effects of synthetic drugs, naturally sourced DAAs targeting SARS‐CoV‐2 should be urgently developed. The SARS‐CoV‐2 main protease (M pro ) can be reasonably used as a drug target because it is crucially involved in viral replication and transcription. To design potent DAAs targeting M pro , the M pro ‐inhibitor binding mechanism, and the conformational changes of M pro induced by inhibitor binding must be essentially clarified. Here, conventional molecular dynamics (cMD) simulation, principal component analysis (PCA), and free energy landscape (FEL) analysis were combined to probe 5 known phytochemical inhibitors‐mediated conformational changes of M pro . The results revealed that inhibitor binding significantly affected the kinetic behavior of M pro , and induced conformational rearrangement. The calculations of binding free energies using molecular mechanics generalized Born surface area (MM‐GBSA) and solvation interaction energy (SIE) methods and residue‐based free energy decomposition values showed that there are 23 key residues involved in the M pro ‐inhibitor binding. We found that one of the tested phytochemicals (PubChem CID 637112) could be used as a potent SARS‐CoV‐2 M pro inhibitor, which can block viral replication and translation.

ChemistrySelectVol. 10(13)
Shandong Normal University (CN), Shandong Jiaotong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Key Technology Research and Development Program of Shandong
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
3.01
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