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

Author:

Bhagat Khushboo1ORCID,Maurya Shweata1ORCID,Yadav Amar Jeet1ORCID,Tripathi Timir2ORCID,Padhi Aditya K.1ORCID

Affiliation:

1. Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering Indian Institute of Technology (BHU) Varanasi Varanasi India

2. Molecular and Structural Biophysics Laboratory, Department of Zoology North‐Eastern Hill University Shillong India

Abstract

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.

Funder

Indian Institute of Technology (BHU) Varanasi

Science and Engineering Research Board

Publisher

Wiley

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