From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery

Abstract

Antimicrobial Peptides (AMPs) are a promising strategy to address bacterial resistance, yet only a limited number have advanced to clinical trials. Recent advances in deep learning provide new opportunities for AMP design. Here, we propose an integrated computational framework combining deep learning with molecular simulation to systematically design and screen novel AMPs. Employing a naïve character-string-based generative adversarial network (GAN), we generated 50 candidate sequences, which were preliminarily screened by the antibacterial peptide discriminative network PGAT-ABPp along with key physicochemical parameters. This screening identified 9 potential functional AMPs. Subsequent molecular dynamics simulations revealed that two peptides can induce water pore formation in bacterial membranes within a limited simulation period, suggesting their potential antibacterial activity. These two peptides were synthesized and tested in vitro, demonstrating efficacy against both Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, thus confirming their clinical potential. This study not only discovered two novel AMPs but also established a cost-effective design strategy, highlighting the broad applicability of this approach for AMP discovery.