Relationship Between Biophysical Properties of Antimicrobial Peptides (AMPs) and their Associated Drug Efficacies

Antibiotic resistance is a growing global health threat. One consequence is that patients with cystic fibrosis (CF) are prone to developing antibiotic resistant lung infections caused by multiple strains of bacteria, including Pseudomonas aeruginosa. Due to the limited number of treatment options for patients with chronic antibiotic resistant infections, there is a need for finding new antibiotics that allow for effective eradication of bacterial infections, such as those in the CF lung. Many antimicrobial peptides (AMPs) have been annotated in databases and are considered as potential alternatives for current antibiotics. However, in many instances, the suitability of AMPs as drug molecules has not been extensively explored. Here, we propose that certain molecular properties of AMPs favor high antibiotic efficacy. Using information from AMP databases, we combined statistical analyses and machine learning techniques to identify relationships between various biophysical properties of AMPs and their drug efficacies. Analyses from classification and regression trees (CART) and random forests suggest that net charge and maximum average hydrophobic moment are the most important properties in determining if a peptide is useful against P. aeruginosa infections in CF patients. Maximum average hydrophobic residue, average alpha helix propensity score, hydrophobic proportion, and peptide length still contribute to this determination but to lesser degrees. Cation-pi interactions, on the other hand, do not appear to factor into this decision at all. Based on these properties, our current work is focused on designing and experimentally testing new peptides that may have activity against P. aeruginosa infections.