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Computational Insights into SARS-CoV-2 Main Protease Mutations and Nirmatrelvir Efficacy: The Effects of P132H and P132H-A173V.

Authors :
Xia YL
Du WW
Li YP
Tao Y
Zhang ZB
Liu SM
Fu YX
Zhang KQ
Liu SQ
Source :
Journal of chemical information and modeling [J Chem Inf Model] 2024 Jul 08; Vol. 64 (13), pp. 5207-5218. Date of Electronic Publication: 2024 Jun 24.
Publication Year :
2024

Abstract

Nirmatrelvir, a pivotal component of the oral antiviral Paxlovid for COVID-19, targets the SARS-CoV-2 main protease (M <superscript>pro</superscript> ) as a covalent inhibitor. Here, we employed combined computational methods to explore how the prevalent Omicron variant mutation P132H, alone and in combination with A173V (P132H-A173V), affects nirmatrelvir's efficacy. Our findings suggest that P132H enhances the noncovalent binding affinity of M <superscript>pro</superscript> for nirmatrelvir, whereas P132H-A173V diminishes it. Although both mutants catalyze the rate-limiting step more efficiently than the wild-type (WT) M <superscript>pro</superscript> , P132H slows the overall rate of covalent bond formation, whereas P132H-A173V accelerates it. Comprehensive analysis of noncovalent and covalent contributions to the overall binding free energy of the covalent complex suggests that P132H likely enhances M <superscript>pro</superscript> sensitivity to nirmatrelvir, while P132H-A173V may confer resistance. Per-residue decompositions of the binding and activation free energies pinpoint key residues that significantly affect the binding affinity and reaction rates, revealing how the mutations modulate these effects. The mutation-induced conformational perturbations alter drug-protein local contact intensities and the electrostatic preorganization of the protein, affecting noncovalent binding affinity and the stability of key reaction states, respectively. Our findings inform the mechanisms of nirmatrelvir resistance and sensitivity, facilitating improved drug design and the detection of resistant strains.

Details

Language :
English
ISSN :
1549-960X
Volume :
64
Issue :
13
Database :
MEDLINE
Journal :
Journal of chemical information and modeling
Publication Type :
Academic Journal
Accession number :
38913174
Full Text :
https://doi.org/10.1021/acs.jcim.4c00334