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

Antimicrobial peptides (AMPs) are promising compounds for the treatment and prevention of multidrug-resistant infections because of their ability to directly disrupt microbial membranes, a mechanism that is less likely to lead to resistance compared to antibiotics. Unfortunately, natural AMPs are prone to proteolytic cleavage in vivo and have relatively low selectivity for microbial versus human cells, motivating the development of synthetic peptidomimetics of AMPs with improved peptide stability, activity, and selectivity. However, a lack of understanding of structure-activity relationships for peptidomimetics constrains development to rational design or experimental predictors, both of which are cost and time prohibitive, especially when the design space of possible sequences scales exponentially with the number of amino acids. To address these challenges, we developed an iterative Gaussian process regression (GPR) approach to explore a large design space of 336 000 synthetic α/β-peptide analogues of a natural AMP, aurein 1.2, based on an initial training set of 147 sequences and their biological activities against microbial pathogens and selectivity for microbes vs. mammalian cells. We show that the quantification of prediction uncertainty provided by GPR can guide the exploration of this design space via 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.