Exploring the binding affinity landscape of SARS-CoV-2 variants: A Computational Approach

Roma Sharma*, Uma Kumari

School of Basic and Applied Science, Career Point University, Kota India

Refer this article

Roma Sharma, Uma Kumari, 2024. Exploring the binding affinity landscape of SARS-CoV-2 variants: a computational approach, V 13 - I 3, Pages- 6566 – 6569. Doi: https://doi.org/10.55522/jmpas.V13I3.6555.

ABSTRACT

The emergence of SARS-CoV-2 variants, like Beta, poses a challenge due to potential changes in viral infectivity and immune escape. This study employs computational tools to analyze the interaction between the Receptor Binding Domain (RBD) of the SARS-CoV-2 Beta variant and the human Angiotensin-Converting Enzyme 2 (ACE2) receptor. Additionally, the potential inhibitory effect of an S304 Fab antibody fragment on this interaction is investigated. Protein structures were visualized using RASMOL/PYMOL and validated with ERRAT/PROCHECK. Docking simulations with the CB-DOCK server were performed to predict the binding mode and affinity of S304 Fab to the Beta variant RBD- ACE2 complex. Our results provide insights into the structural features of the Beta variant RBD-ACE2 interaction and predict potential binding sites for the S304 Fab fragment. The findings contribute to understanding viral entry mechanisms for the Beta variant and suggest S304 Fab as a likely candidate for further investigation as a therapeutic strategy.

Keywords:

Sars- CoV-2, ACE2, S304 Fab Fragment, RBD, Molecular Docking.

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