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

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.