Computational optimization of a pan-coronavirus fusion inhibitory peptide targeting spike’s heptapeptide repeat region

Abstract

In the past two decades, highly pathogenic coronaviruses (CoVs), such as severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have constituted a grave threat to human health. Broad-spectrum anti-CoV fusion inhibitors that target the heptapeptide repeat (HR) region within the S2 subunit of SARS-CoV-2 spike (S) protein exhibit inhibitory activity against various CoVs. In this study, we employed EK1, a fusion inhibitor previously characterized for its broad spectrum and potent antiviral activity, as a scaffold for computational design to enhance its inhibitory potential using the Rosetta software suite. We designed EK1 variants and synthesized two N-terminally extended EK1 elongation peptides, and evaluated their inhibitory activity. The results revealed that the designed peptides enhanced inhibitory activity against diverse CoVs. Structural analysis and molecular dynamics simulations demonstrated that EK1 variants formed more robust interactions with HR1 of SARS-CoV-2, and these interactions were conserved across different CoVs. These findings underscore the utility of computational approaches in optimizing therapeutic peptides.