Evolutionary Design of Membrane-Lytic Antimicrobial Peptides with Mixture of Experts

Antimicrobial peptides (AMPs) hold great promise in combating drug-resistant pathogens, yet current design models do not explicitly account for their primary mode of action, membrane disruption. To address this gap, we propose AMPainterV2, a model that designs membrane-lytic AMPs using generative adversarial imitation learning. AM-PainterV2 integrates a Mixture-of-Experts (MoE) policy network to support insertion, deletion, and mutation operations, thereby expanding the evolutionary design space. In vitro experiments demonstrate that all ten top-ranked peptides evolved from random sequences are active membrane-lytic AMPs, with the best candidate achieving a mean minimal inhibitory concentration (MIC) of 1.5 {micro}M and superior membrane-lytic activity compared to polymyxin B. Particularly, a deletion-only version of AMPainterV2 enables membrane-lytic AMP miniaturization, yielding six miniaturized peptides with comparable or improved activity and an average 36% length reduction. Collectively, AMPainterV2 advances mechanism-driven AMP design and offers a valuable tool for cost-effective AMP development.