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 K_D 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 (SPB25_F8N, SPB25_F8R, SPB25_L25R, SPB25_F8N/L25R, and SPB25_F8R/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.