Using In Silico Bioinformatics Algorithms for the Accurate Prediction of the Impact of Spike Protein Mutations on the Pathogenicity, Stability, and Functionality of the SARS-CoV-2 Virus and Analysis of Potential Therapeutic Targets

Abstract

Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have used bioinformatics to investigate seventeen mutations in the spike protein of SARS-CoV-2, as this mediates infection of human cells and is the target of most vaccine strategies and antibody-based therapies. Two mutations, H146Y and S221W, were identified as being most pathogenic. Mutations at positions D614G, A829T, and P1263L might also have deleterious effects on protein function. We hypothesized that candidate small molecules may be repurposed to combat viral infection. We investigated changes in binding energies of the ligands and the mutant proteins by assessing molecular docking. For an understanding of cellular function and organization, protein-protein interactions are also critical. Protein-protein docking for naive and mutated structures of SARS-CoV-2 S protein was evaluated for their binding energy with the angiotensin-converting enzyme 2 (ACE2). These interactions might limit the binding of the SARS-CoV-2 spike protein to the ACE2 receptor or may have a deleterious effect on protein function that may limit infection. These results may have important implications for the transmission of SARS-CoV-2, its pathogenesis, and the potential for drug repurposing and immune therapies.