Computational Analysis of Mutations in the Receptor-Binding Domain of SARS-CoV-2 Spike and Their Effects on Antibody Binding

Abstract

Currently, SARS-CoV-2 causing coronavirus disease 2019 (COVID-19) is responsible for one of the most deleterious pandemics of our time. The interaction between the ACE2 receptors at the surface of human cells and the viral Spike (S) protein triggers the infection, making the receptor-binding domain (RBD) of the SARS-CoV-2 S-protein a focal target for the neutralizing antibodies (Abs). Despite the recent progress in the development and deployment of vaccines, the emergence of novel variants of SARS-CoV-2 insensitive to Abs produced in response to the vaccine administration and/or monoclonal ones represent a potential danger. Here, we analyzed the diversity of neutralizing Ab epitopes and assessed the possible effects of single and multiple mutations in the RBD of SARS-CoV-2 S-protein on its binding affinity to various antibodies and the human ACE2 receptor using bioinformatics approaches. The RBD-Ab complexes with experimentally resolved structures were grouped into four clusters with distinct features at sequence and structure level. The performed computational analysis indicates that while single amino acid replacements in RBD may only cause partial impairment of the Abs binding, moreover, limited to specific epitopes, the variants of SARS-CoV-2 with multiple mutations, including some which were already detected in the population, may potentially result in a much broader antigenic escape. Further analysis of the existing RBD variants pointed to the trade-off between ACE2 binding and antigenic escape as a key limiting factor for the emergence of novel SAR-CoV-2 strains, as the naturally occurring mutations in RBD tend to reduce its binding affinity to Abs but not to ACE2. The results provide guidelines for further experimental studies aiming to identify high-risk RBD mutations that allow for an antigenic escape.

Keywords

• coronaviruses
• virus–host interactions
• binding free energy
• antigenic escape
• computational mutagenesis
• hACE2