Bebtelovimab‐bound SARS‐CoV‐2 RBD mutants: resistance profiling and validation with escape mutations, clinical results, and viral genome sequences

The dynamic evolution of SARS‐CoV‐2 variants necessitates ongoing advancements in therapeutic strategies. Despite the promise of monoclonal antibody (mAb) therapies like bebtelovimab, concerns persist regarding resistance mutations, particularly single‐to‐multipoint mutations in the receptor‐binding domain (RBD). Our study addresses this by employing interface‐guided computational protein design to predict potential bebtelovimab‐resistance mutations. Through extensive physicochemical analysis, mutational preferences, precision‐recall metrics, protein–protein docking, and energetic analyses, combined with all‐atom, and coarse‐grained molecular dynamics (MD) simulations, we elucidated the structural‐dynamics‐binding features of the bebtelovimab–RBD complexes. Identification of susceptible RBD residues under positive selection pressure, coupled with validation against bebtelovimab‐escape mutations, clinically reported resistance mutations, and viral genomic sequences enhances the translational significance of our findings and contributes to a better understanding of the resistance mechanisms of SARS‐CoV‐2. This study uncovers single‐to‐multipoint resistance mutations in the SARS‐CoV‐2 receptor binding domain (RBD) against bebtelovimab through an integrated approach. Utilizing computational protein design, physicochemical feature analysis, precision‐recall metrics, energetic assessment, and all‐atom and coarse‐grained molecular dynamics simulations, it highlights adaptation hotspots, mutational tendencies, intermolecular interactions, and correlations with clinical and experimental data related to bebtelovimab resistance.