An explainable few-shot learning model for the directed evolution of antimicrobial peptides

Abstract

Due to the persistent threat of antibiotic resistance posed by Gram-negative pathogens, the discovery of new antimicrobial agents is of critical importance. In this study, we employed deep learning-guided directed evolution to explore the chemical space of antimicrobial peptides (AMPs), which present promising alternatives to traditional small-molecule antibiotics. Utilizing a fine-tuned protein language model tailored for small dataset learning, we achieved structural modifications of the lipopolysaccharide-binding domain (LBD) derived from Marsupenaeus japonicus, a prawn species of considerable value in aquaculture and commercial fisheries. The engineered LBDs demonstrated exceptional activity against a range of Gram-negative pathogens. Drawing inspiration from evolutionary principles, we elucidated the bactericidal mechanism through molecular dynamics simulations and mapped the directed evolution pathways using a ladderpath framework. This work highlights the efficacy of explainable few-shot learning in the rational design of AMPs through directed evolution.