Computational prediction of the effect of mutations in the receptor-binding domain on the interaction between SARS-CoV-2 and human ACE2
- PMID: 35138508
- DOI: 10.1007/s11030-022-10392-x
Computational prediction of the effect of mutations in the receptor-binding domain on the interaction between SARS-CoV-2 and human ACE2
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 continues to mutate. Numerous studies have indicated that this viral mutation, particularly in the receptor-binding domain area, may increase the viral affinity for human angiotensin-converting enzyme 2 (hACE2), the receptor for viral entry into host cells, thereby increasing viral virulence and transmission. In this study, we investigated the binding affinity of SARS-CoV-2 variants (Delta plus, Iota, Kappa, Mu, Lambda, and C.1.2) on hACE2 using computational modeling with a protein-protein docking approach. The simulation results indicated that there were differences in the interactions between the RBD and hACE2, including hydrogen bonding, salt bridge interactions, non-bonded interactions, and binding free energy differences among these variants. Molecular dynamics simulations revealed that mutations in the RBD increase the stability of the hACE2-spike protein complex relative to the wild type, following the global stability trend and increasing the binding affinity. The value of binding-free energy calculated using molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) indicated that all mutations in the spike protein increased the contagiousness of SARS-CoV-2 variants. The findings of this study provide a foundation for developing effective interventions against these variants. Computational modeling elucidates that the spike protein of SARS-CoV-2 variants binds considerably stronger than the wild-type to hACE2.
Keywords: In Silico; Mutation; Receptor-binding domain; SARS-CoV-2; Spike; hACE.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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