Computational design of stapled peptide inhibitor against SARS CoV-2 receptor-binding domain

The SARS CoV-2 interacts with the human ACE2 receptor through its Receptor Binding Domain (RBD) to attach to the cell surface and transfers its genetic material. A continuous effort is going on to find a suitable inhibitor to prevent this association. In this investigation, we will use a computational approach to predict model stapled peptides derived from the human ACE2 domain which can inhibit the ACE2-RBD binding, competitively. A strategy of crosslinking suitable amino acids of ACE2 will be used to design stapled peptides followed by an estimation of their binding affinity with viral RBD. The types of stapling agent and their points of attachment to the peptide will be varied to enhance the binding affinity. In addition to that, the impact of glycan shielding and mutation of the RBD residues on the binding of these peptides will also be investigated in atomic detail.