Screening for Peptides to Bind and Functionally Inhibit SARS-CoV-2 Fusion Peptide Using Mirrored Combinatorial Phage Display and Human Proteomic Phage Display

TL;DR

Researchers used phage display to find peptides that could bind to a key part of the SARS-CoV-2 virus, but found that these peptides did not strongly inhibit infection, though they helped develop a computational model for future drug design.

Contribution

The study introduces a computational model for pancoronaviral fusion peptide disruptors derived from phage display screening failures.

Findings

Ten D-peptides identified from combinatorial phage display did not inhibit SARS-CoV-2 infection in Vero-E6/TMPRSS2 cells.

Two overlapping 14mer peptides from OTUD1 were identified using a proteomic phage display library.

Molecular dynamics modeling revealed a stable binding mode between OTUD1 peptides and the SARS-CoV-2 fusion peptide.

Abstract

To identify pancoronaviral inhibitors, we sought to identify peptides that bound the evolutionarily conserved SARS-CoV-2 spike fusion peptide (FP). We screened the NEB PhD-7-mer random combinatorial phage display library against FP, synthesised as a D-peptide, to identify peptides from the L-library to be synthesised as proteolytically resistant D peptides. We selected the top ten peptides that were not seen in another published screen with this library, as these were more likely to be specific. All ten D-peptides had no impact on the infection of Vero-E6/TMPRSS2 cells by SARS-CoV-2. Screening of a proteomic-derived phage display library from the disordered regions of human proteins identified two overlapping 14mer peptides from a region of OTUD1. While a synthetic peptide based on their sequences failed to markedly inhibit viral entry, molecular dynamics structural modelling highlighted a stable binding mode where positive residues on one side of the OTUD1 helix interacted with hydrophobic residues of the FP triple-helical wedge. Thus, while the two phage display strategies failed to yield peptide sequences that are themselves strong inhibitors of viral infection, they led to the development of a computational model that can underpin future designs of potential pancoronaviral FP disruptors.

Figures