Antimicrobial peptides as computational candidates for SARS-CoV-2 papain-like protease inhibition

Abstract

SARS-CoV-2 papain-like protease (PLpro) remains an attractive antiviral target because of its dual role in viral polyprotein processing and host immune modulation. In this study, we employed an exploratory structure-based computational workflow to identify antimicrobial peptide scaffolds with predicted PLpro-binding potential. A total of 6,615 peptides compiled from public databases were screened using protein-peptide docking, followed by molecular dynamics simulations and end-state binding energy analysis for selected complexes. Among the prioritized candidates, peptides 33,035 and 17,047 showed favorable predicted occupation of the catalytic region and persistent interactions with residues in the active-site environment. Molecular dynamics analyses suggested sustained peptide engagement within the PLpro binding region, although distinct degrees of conformational adaptation were observed across systems. MM/PBSA calculations provided additional energetic support for peptide–PLpro association in the selected complexes. In silico toxicity prediction further indicated that peptide 33,035 may require sequence optimization before translational consideration. Overall, our results identify peptide scaffolds worthy of future biochemical and antiviral validation, while reinforcing the need for experimental confirmation and further optimization before any therapeutic inference can be made.