Computational Estimation of Residues Involving Resistance to the SARS-CoV-2 Main Protease Inhibitor Ensitrelvir Based on Virtual Alanine Scan of the Active Site.

Abstract

Ensitrelvir is a noncovalent inhibitor of the main protease (Mpro) of severe acute respiratory syndrome coronavirus 2. Acquisition of drug resistance in virus-derived proteins is a serious therapeutic concern, and drug resistance occurs due to amino acid mutations. In this study, we computationally constructed 24 mutants, in which one residue around the active site was replaced with alanine and performed molecular dynamics simulations to the complex of Mpro and ensitrelvir to predict the residues involved in drug resistance. We evaluated the changes in the entire protein structure and ligand configuration in each of these mutants and estimated which residues were involved in ensitrelvir recognition. This method is called a virtual alanine scan. In nine mutants (S1A, T26A, H41A, M49A, L141A, H163A, E166A, V186A, and R188A), although the entire protein structure and catalytic dyad (cysteine (Cys)145 and histidine (His)41) were not significantly moved, the ensitrelvir configuration changed. Thus, it is considered that these mutants did not recognize ensitrelvir while maintaining Mpro enzymatic activities, and Ser1, Thr26, His41, Met49, Leu141, His163, Glu166, Val186, and Arg188 may be related to ensitrelvir resistance. The ligand shift noted in M49A was similar to that observed in M49I, which has been shown to be experimentally ensitrelvir resistant. These findings suggest that our research approach can predict mutations that incite drug resistance.