# Innovative Peptide Therapeutics for SARS-CoV-2: Design, Docking, and Functional Analysis

## TL;DR

This paper presents a computational approach to design new peptide drugs that target key SARS-CoV-2 proteins, showing strong potential for treating the virus.

## Contribution

The study introduces novel peptide analogs with high binding affinity for conserved SARS-CoV-2 proteins using an integrated computational strategy.

## Key findings

- Peptide W showed the strongest docking score (-303.41 a.u.) for inhibiting the S protein compared to the EK1 lead peptide.
- Peptide A5 and A7 were top candidates for RdRp and N protein inhibition with scores of -187.36 a.u. and -317.69 a.u., respectively.
- MD simulations confirmed the stability of the W and A5 complexes, maintaining key interactions over time.

## Abstract

The continuous emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants necessitates the rapid development of novel therapeutics, particularly those targeting conserved viral proteins. Peptide-based drugs offer high specificity and low toxicity, making them ideal candidates.

This study employed an integrated computational approach, combining structural biology, molecular docking, and molecular dynamics (MD) simulations, to design and evaluate novel peptide analogs targeting three key proteins of SARS-CoV-2: the Spike (S) protein, RNA-dependent RNA polymerase (RdRp), and nucleocapsid (N) protein.

The first step involved preparing a dataset containing anti-SARS-CoV-2 peptides using the DRAVP database and a literature survey. Then, the best inhibitory peptides were screened using the AVPPred tool, and analogous peptides were designed based on the selected lead peptide. The designed peptides were then investigated in terms of their structure, physicochemical properties, and antiviral potency. Additionally, molecular docking, performed using the specialized nCoVDock2 server, showed that all designed analogs exhibited highly favorable binding. Specifically, the best-performing analogs achieved remarkable docking scores in the range of -200 to -300 a.u. (arbitrary units), indicating a strong predicted relative binding affinity for their respective targets. The top-ranked complexes were then subjected to 100 ns explicit solvent MD simulations.

Our findings suggest that peptide W is the most effective analogue for inhibiting S protein, achieving a relative docking score of -303.41 a.u., in contrast to the -284.12 a.u. relative docking score of the EK1 lead peptide. Regarding the inhibition of RdRp protein, the top newly designed analogue is peptide A5, which has a relative docking score of -187.36 a.u., compared to the score of -121.3 a.u. for lead peptide 5, respectively. The leading novel analogue for inhibiting the N protein is A7, which has a relative docking score of -317.69 a.u., surpassing the relative docking score of -255.48 a.u. for Plectasin. The MD results confirmed the high dynamic stability of the W (targeting S protein) and A5 (targeting RdRp) complexes, demonstrating low Root Mean Square Deviation (RMSD) and maintaining critical hydrogen bonds and hydrophobic interactions throughout the trajectory.

The use of bioinformatics algorithms to develop engineered peptides with high affinity for SARS-CoV-2 virulence proteins offers a promising outlook for peptide-based therapies against SARS-CoV-2. It also presents a promising approach for developing therapeutic methods against other viral diseases. Furthermore, these computational insights lay the groundwork for subsequent in vitro and in vivo validation studies to ascertain the therapeutic efficacy and safety profiles of the identified peptide candidates.