Antimicrobial Peptide Identified via Machine Learning Presents Both Potent Antibacterial Properties and Low Toxicity toward Human Cells

Abstract

Preventing infection is a critical clinical challenge; however, the extensive use of antibiotics has resulted in remarkably increased antibiotic resistance. A variety of antibiotic alternatives including antimicrobial peptides (AMPs) have been studied. Unfortunately, like most conventional antibiotics, most current AMPs have shown significantly high toxicity toward the host, and therefore induce compromised host responses that may lead to negative clinical outcomes such as delayed wound healing. In this study, one of the AMPs with a short length of nine amino acids was first identified via machine learning to present potentially low cytotoxicity, and then synthesized and validated in vitro against both bacteria and mammalian cells. It was found that this short AMP presented strong and fast-acting antimicrobial properties against bacteria like Staphylococcus aureus, one of the most common bacteria clinically, and it targeted and depolarized bacterial membranes. This AMP also demonstrated significantly lower (e.g., 30%) toxicity toward mammalian cells like osteoblasts, which are important cells for new bone formation, compared to conventional antibiotics like gentamicin, vancomycin, rifampin, cefazolin, and fusidic acid at short treatment times (e.g., 2 h). In addition, this short AMP demonstrated relatively low toxicity, similar to osteoblasts, toward an epithelial cell line like BEAS-2B cells.