(untitled) (Communications in Computational Chemistry, 2025)
Mutations that cause structural changes in proteins can sometimes reduce drug efficacy dramatically, a phenomenon known as mutation-induced drug resistance. For example, emerging drug-resistant mutations in the SARS-CoV-2 main protease (M^pro) threaten the long-term efficacy of nirmatrelvir, the active component of Paxlovid. Various methods have been developed to predict the impact of such mutations, with differing levels of reliability. In this study, comparative binding free energy calculations using Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) and Alchemical Transformation (also known as Free Energy Perturbation, or FEP) were performed to assess five naturally occurring Mpro mutations (SER144ALA, MET165ALA, GLU166ALA, HIE172ALA, and GLN192ALA) at the nirmatrelvir binding site. The results reveal a weak correlation (R_Pearson=0.18) between MM/PBSA predictions and experimental data. In contrast, FEP calculations using either the Multistate Bennett Acceptance Ratio (MBAR) or Thermodynamic Integration (TI) yield stronger linear correlations (R_Pearson = 0.56 and 0.57, respectively). This study highlights the superior reliability of FEP in quantifying binding affinity losses due to drug resistance and underscores its potential for the proactive surveillance of clinical resistance mutations. Moreover, such insights are crucial for advancing antiviral drug development and guiding the design of inhibitors with a reduced risk of resistance evolution.