Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens

Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs. Of these, the 6 most effective were synthesized and tested against multidrug-resistant pathogens and demonstrated activity against carbapenem-resistant species Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii, and vancomycin-resistant Enterococcus faecium. The most potent AMP, pep-19-mod, was validated in vivo, achieving over 95% reduction in bacterial loads in mouse models of thigh infection through both systemic and local administration. Our approach advances the automatic identification and optimization of AMPs.