Machine learning-driven discovery of highly selective antifungal peptides containing non-canonical β-amino acids

iterative experimental measurements to efficiently discover novel sequences with up to a 52-fold increase in antifungal selectivity compared to aurein 1.2. The highest selectivity peptide discovered using this approach features an unconventional substitution of cationic amino acids in the hydrophobic face and would be unlikely to be explored by conventional rational design. Overall, this work demonstrates a generalizable approach that integrates computation and experiment to accurately predict the selectivity of AMPs containing synthetic amino acids, which we employed to discover new α/β-peptides that hold promise as selective antifungal agents to combat the antimicrobial resistance crisis.