Bioinformatics analysis of the differences in the binding profile of the wild-type and mutants of the SARS-CoV-2 spike protein variants with the ACE2 receptor

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Reports of new variants that potentially increase virulence and viral transmission, as well as reduce the efficacy of available vaccines, have recently emerged. In this study, we computationally analyzed the N439K, S477 N, and T478K variants for their ability to bind Angiotensin-converting enzyme 2 (ACE2). We used the protein-protein docking approach to explore whether the three variants displayed a higher binding affinity to the ACE2 receptor than the wild type. We found that these variants alter the hydrogen bonding network and the cluster of interactions. Additional salt bridges, hydrogen bonds, and a high number of non-bonded contacts (i.e., non-bonded interactions between atoms in the same molecule and those in other molecules) were observed only in the mutant complexes, allowing efficient binding to the ACE2 receptor. Furthermore, we used a 2.0-μs all-atoms simulation approach to detect differences in the structural dynamic features of the resulting protein complexes. Our findings revealed that the mutant complexes possessed stable dynamics, consistent with the global trend of mutations yielding variants with improved stability and enhanced affinity. Binding energy calculations based on molecular mechanics/generalized Born surface area (MM/GBSA) further revealed that electrostatic interactions principally increased net binding energies. The stability and binding energies of N439K, S477 N, and T478K variants were enhanced compared to the wild-type-ACE2 complex. The net binding energy of the systems was -31.86 kcal/mol for the wild-type-ACE2 complex, -67.85 kcal/mol for N439K, -69.82 kcal/mol for S477 N, and -69.64 kcal/mol for T478K. The current study provides a basis for exploring the enhanced binding abilities and structural features of SARS-CoV-2 variants to design novel therapeutics against the virus.

Keywords: Docking; Free energy; SARS-CoV-2; Simulation; Variants.