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Facial amphiphilicity is an extraordinary chemical structure feature of a variety of antimicrobial peptides and polymers. Vast efforts have been dedicated to small molecular, macromolecular and dendrimer-like systems to mimic this highly preferred structure or conformation, including local facial amphiphilicity and global amphiphilicity. This work conceptualizes Facial Amphiphilicity Index (FAI) as a numerical value to quantitatively characterize the measure of chemical compositions and structural features in dictating antimicrobial efficacy. FAI is a ratio of numbers of charges to rings, representing both compositions of hydrophilicity and hydrophobicity. Cationic derivatives of multicyclic compounds were evaluated as model systems for testing antimicrobial selectivity against Gram-negative and Gram-positive bacteria. Both monocyclic and bicyclic compounds are nonantimicrobial regardless of FAIs. Antimicrobial efficacy was observed with systems having larger cross-sectional areas including tricyclic abietic acid and tetracyclic bile acid. While low and high FAIs respectively lead to higher and lower antimicrobial efficacy, in consideration of cytotoxicity, the sweet spot is typically suited with intermediate FAIs for each specific system. This can be well explained by the synergistic hydrophobic-hydrophobic and electrostatic interactions with bacterial cell membranes and the difference between bacterial and mammalian cell membranes. The adoption of FAI would pave a new avenue toward the design of next-generation antimicrobial macromolecules and peptides. 

Abstract Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. We further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis.