De novo design of self-assembling peptides with antimicrobial activity guided by deep learning

Abstract

Bioinspired materials based on self-assembling peptides are promising for tackling various challenges in biomedical engineering. While contemporary data-driven approaches have led to the discovery of self-assembling peptides with various structures and properties, predicting the functionalities of these materials is still challenging. Here we describe the deep learning-guided de novo design of antimicrobial materials based on self-assembling peptides targeting bacterial membranes to address the emerging problem of bacterial drug resistance. Our approach integrates non-natural amino acids for enhanced peptide self-assembly and effectively predicts the functional activity of the self-assembling peptide materials with minimal experimental annotation. The designed self-assembling peptide leader displays excellent in vivo therapeutic efficacy against intestinal bacterial infection in mice. Moreover, it exhibits an enhanced biofilm eradication capability and does not induce acquired drug resistance. Mechanistic studies reveal that the designed peptide can self-assemble on bacterial membranes to form nanofibrous structures for killing multidrug-resistant bacteria. This work thus provides a strategy to discover functional peptide materials by customized design.