A hybrid evolutionary and structural method for AI-guided peptide inhibitor design using AlphaFold and Rosetta.

Peptide inhibitors represent a promising class of antiviral therapeutics, offering several advantages over traditional small-molecule drugs, including low toxicity, high specificity, and biocompatibility. However, rational and efficient design and optimization of inhibitor peptides remains a significant challenge to current methods. Here we show EvoPepFold, a genetic algorithm-based framework designed to generate inhibitory peptides. We evaluated EvoPepFold to design and optimize peptides targeting the SARS-CoV-2 main protease (Mpro). EvoPepFold was applied through two complementary strategies: molecular docking using the Rosetta suite, and peptide 3D modeling with ColabFold. The top candidates were further evaluated through molecular dynamics simulations to assess stability and interaction energy. Our results demonstrate that EvoPepFold successfully identified peptides with favorable binding affinities and stable protein-peptide interactions. These findings highlight the potential of evolutionary algorithms in guiding the rational design of peptide-based antivirals, contributing to ongoing efforts in peptide engineering for therapeutic applications.