Improvement of native structure-based peptides as efficient inhibitors of protein-protein interactions of SARS-CoV-2 spike protein and human ACE2

Abstract

New pathogens responsible for novel human disease outbreaks in the last two decades are mainly the respiratory system viruses. Not different was the last pandemic episode, caused by infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One of the extensively explored targets, in the recent scientific literature, as a possible way for rapid development of COVID-19 specific drug(s) is the interaction between the receptor-binding domain of the virus' spike (S) glycoprotein and human receptor angiotensin-converting enzyme 2 (hACE2). This protein-protein recognition process is involved in the early stages of the SARS-CoV-2 life cycle leading to the host cell membrane penetration. Thus, disrupting this interaction may block or significantly reduce the infection caused by the novel pathogen. Previously we have designed (by in silico structure-based analysis) three very short peptides having sequences inspirited by hACE2 native fragments, which effectively bind to the SARS-CoV-2 S protein and block its interaction with the human receptor. In continuation of the above mentioned studies, here we presented an application of molecular modeling approach resulting in improved binding affinity of the previously proposed ligand and its enhanced ability to inhibit meaningful host-virus protein-protein interaction. The new optimized hexapeptide binds to the virus protein with affinity one magnitude higher than the initial ligand and, as a very short peptide, has also great potential for further drug development. The peptide-based strategy is rapid and cost-effective for developing and optimizing efficient protein-protein interactions disruptors and may be successfully applied to discover antiviral candidates against other future emerging human viral infections.

Keywords: ACE2; COVID-19; SARS-CoV-2; angiotensin-converting enzyme-2; coronavirus; drug design; inhibitors of protein-protein interactions; peptides.

FIGURE 1

Scheme of peptide ligands optimization procedure. The amino acid residues with most favored changes in ΔG_bind for each run are shown in red, and bold fonts represent the advantageous mutation(s) adopted from the previous run.

FIGURE 2

Inhibition of SARS-CoV-2-S-RBD binding to hACE2 by various concentrations of the peptide J3. Graph represents data from three independent biological replicates.

FIGURE 3

RBD and hACE2 interaction inhibition assay for best peptides binders J3 and J3.2 monitored by MST pseudo-titration experiments. Two components experiment without peptides (upper panel). Three component experiment repeated in the presence of peptide J3 (middle panel) and peptide J3.2 (lower panel) at a constant concentration of 1 mM. The concentration of hACE2 (unlabeled) varied from 30 pM to maximum 2.5 µM, whereas RBD (His-Tagged) was kept constant at 50 nM. Black lines represent the model fitted for each peptide globally using data from at least three independent MST pseudo-titration experiments, while blue lines denote the 95% confidence bands for the fitted line.

FIGURE 4

BEAS 2B and Calu-3 cells grown in a 96-multiwell plates were exposed for 72 h to J3 peptide applied at various concentrations ranging from 1 nM to 1 mM. Cell viability was determined by MTS assay. Values are represented as mean ± SD of three independent experiments.

FIGURE 5

The predicted complex structures of peptides pep1d (red) and J3 (green) with SARS-CoV-2 spike receptor-binding domain (blue). Residues creating interaction interface area with hACE2 are coloured yellow. Detailed view of interactions between pep1d (lower right panel) and J3 (lower left panel).