Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Abstract

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell-free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for high throughput and low-cost production and testing of bioactive peptides within less than 24 h.

Abstract

Deep learning holds a great promise for the discovery and design of bioactive peptides, but experimental approaches to validate candidates in high throughput and at low cost are needed. Here, the authors combine deep learning and cell free biosynthesis for antimicrobial peptide (AMP) development and identify 30 functional AMPs, of which six with broad-spectrum activity against drug-resistant pathogens.