Establishing Quantifiable Guidelines for Antimicrobial α/β-Peptide Design: A Partial Least-Squares Approach to...

Antimicrobial peptides (AMPs) are promising candidates to combat pathogens that are resistant to conventional antimicrobial drugs because they operate through mechanisms that involve membrane disruption. However, the use of AMPs in clinical settings has been limited, at least in part, by their susceptibility to proteolytic degradation and their lack of selectivity toward pathogenic microbes vs mammalian cells. We recently reported on the design of α- and β-peptide oligomers structurally templated upon the naturally occurring α-helical AMP aurein 1.2. These α/β-peptide oligomers are more proteolytically stable than aurein 1.2 and have several other attributes that render them attractive as alternatives to conventional AMPs. This study describes the influence of peptide physicochemical properties on the broad-spectrum activity of aurein 1.2-based α/β-peptide mimics against nine bacterial, fungal, and mammalian cell lines. We used a partial least-squares regression (PLSR)-supervised machine learning model to quantify and visualize relationships between experimentally determined physicochemical properties (e.g., hydrophobicity, charge, and helicity) and experimentally measured cell-type-specific activities of 21 peptides in a 149-member α/β-peptide library. Using this approach, we identified several peptides that were predicted to exhibit enhanced broad-spectrum selectivity, a measure that evaluates antimicrobial activity relative to mammalian cell toxicity compared to aurein 1.2. Experimental validation demonstrated high model predictive performance, and characterization of compounds with the highest broad-spectrum selectivity revealed peptide hydrophobicity, helicity, and helical rigidity to be strong predictors of broad-spectrum selectivity. The most selective peptide identified from the model prediction has more than a 13-fold improvement in broad-spectrum selectivity than that of aurein 1.2, demonstrating the ability of using PLSR models to identify quantitative structure-function relationships for nonstandard amino acid-containing peptides. Overall, this work establishes quantifiable guidelines for the rational design of helical antimicrobial α/β-peptides and identifies promising new α/β-peptides with significantly reduced mammalian toxicities and improved antifungal and antibacterial activities relative to aurein 1.2.