Computational Design of 25-mer Peptide Binders of SARS-CoV-2.

Abstract

SARS-CoV-2 is the novel coronavirus causing the COVID-19 pandemic. To enter human cells, the receptor-binding domain (RBD) of the S1 subunit of SARS-CoV-2 (SARS-CoV-2-RBD) initially binds to the peptidase domain of angiotensin-converting enzyme 2 receptor (ACE2-PD). Using peptides to inhibit SARS-CoV-2-RBD binding to ACE2 is a potential therapeutic solution for COVID-19. A previous study identified a 23-mer peptide (SBP1) that bound to SARS-CoV-2-RBD with comparable KD to ACE2. We employed computational protein design and molecular dynamics (MD) to design SARS-CoV-2-RBD 25-mer peptide binders (SPB25) with better predicted binding affinity than SBP1. Using residues 21–45 of the α1 helix of ACE2-PD as the template, our strategy is employing Rosetta to enhance SPB25 binding affinity to SARS-CoV-2-RBD and avoid disrupting existing favorable interactions by using residues that have not been reported to form favorable interactions with SARS-CoV-2-RBD as designed positions. Designed peptides with better predicted binding affinities, by Rosetta, than SPB25 were subjected to MD validation. The MD results show that five designed peptides (SPB25F8N, SPB25F8R, SPB25L25R, SPB25F8N/L25R, and SPB25F8R/L25R) have better predicted binding affinities, by the MM-GBSA method, than SPB25 and SBP1. This study developed an approach to design SARS-CoV-2-RBD peptide binders, and these peptides may be promising candidates as potential SARS-CoV-2 inhibitors.