Small Peptides Inhibition of SARS-CoV-2 Mpro via Computational Approaches

Background: The application of molecular docking and Machine Learning (ML) calculations in evaluating peptide-based inhibitors allows for the systematic investigation of sequenceactivity relationships, guiding the design of potent peptides with optimal binding characteristics. Objective: This study aimed to screen short peptides using computational simulation to identify promising inhibitors against SARS-CoV-2 Mpro. Methods: Short peptides were screened using molecular docking to identify promising candidates. The ML model was applied to confirm the docking outcome. The PreADME server was then used to analyze the HIA and toxicity of the peptides. Results: 168,420 short peptides were docked to identify 5 tetrapeptides with promising docking scores against SARS-CoV-2 Mpro including, PYPW, WWPF, WWPY, HYPW, and WYPF. The obtained results were also confirmed via ML calculations. The analyses highlighted the importance of residues Thr190 and Asn142 that are crucial in the binding process. All of top-lead peptides adopt low toxicity and can be absorbed via the human intestine. They can also cross the blood brain barier. Conclusion: This work enhances our understanding of Mpro interactions and informs future ligand design, contributing to the development of therapeutic strategies against COVID-19.