Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, 
represent an untapped source of promising antibiotic alternatives. However, 
bacteriocins display diverse mechanisms of action, a narrow spectrum of 
activity, and inherent challenges in natural product isolation making in vitro 
verification of putative bacteriocins difficult. A subset of bacteriocins exert 
their antimicrobial effects through favorable biophysical interactions with the 
bacterial membrane mediated by the charge, hydrophobicity, and conformation of 
the peptide. We have developed a pipeline for bacteriocin-derived compound 
design and testing that combines sequence-free prediction of bacteriocins using 
machine learning and a simple biophysical trait filter to generate 20 amino acid 
peptides that can be synthesized and evaluated for activity. We generated 28,895 
total 20-mer candidate peptides and scored them for charge, α-helicity, and 
hydrophobic moment. Of those, we selected 16 sequences for synthesis and 
evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides 
with the overall highest scores for our biophysical parameters exhibited 
significant antimicrobial activity against Escherichia coli and Pseudomonas 
aeruginosa. Our combined method incorporates machine learning and 
biophysical-based minimal region determination to create an original approach to 
swiftly discover bacteriocin candidates amenable to rapid synthesis and 
evaluation for therapeutic use.