ProT‐Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models

Abstract Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic‐resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT‐Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT‐Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty‐four peptides showed antimicrobial activity against both gram‐positive or gram‐negative bacteria. Among broad‐spectrum peptides, AMP₂ exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug‐resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user‐friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide‐based drug candidates in the future, as well as proteins with tailored characteristics.