Computational Estimation of the Residues Involved in the Emergence of Drug Resistance to the SARS-CoV-2 Main Protease Inhibitor Nirmatrelvir using a Virtual Alanine Scan

Nirmatrelvir is a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitor that exerts its antiviral activity by covalently binding to the catalytic cysteine (Cys145) of Mpro; however, the emergence of drug-resistant variants remains an obstacle to successful antiviral therapy. In this study, we established 23 artificial SARS-CoV-2 Mpro mutants by substituting each active-site residue with alanine in the Mpro–nirmatrelvir complex using a computational approach referred to as a virtual alanine scan. Although the methods were primarily used for non-covalent inhibitor complexes, we conducted a virtual alanine scan for a protein–covalent inhibitor complex. Mutants, in which the structural changes of the main chain and the catalytic dyad were minimal, while the ligand configuration was significantly shifted, were considered to potentially confer drug resistance. The analysis revealed 13 residues: Ser1, His41, Tyr54, Phe140, Leu141, Gly143, Ser144, Cys145, Met165, Glu166, Pro168, Gln189, and Gln192 that are important for the recognition of nirmatrelvir by Mpro, and mutations at these residues may result in drug resistance. The ligand shifts observed in the experimentally reported resistant mutant G143S and the artificially mutant G143A were very similar. These results also indicate that a virtual alanine scan can be applied to covalent inhibitors.