Machine learning-guided directed evolution for the development of small-molecule antibiotics originating from antimicrobial peptides

Abstract

Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics that possess a variety of potent biological activities by exerting immunomodulatory effects to clear difficult-to-treat infections. Understanding the structure-activity relationships (SARs) of AMPs can direct the synthesis of desirable therapeutics. In this study, we use machine learning-guided directed evolution to develop the lipopolysaccharide-binding domain (LBD), which acts as a functional domain of anti-lipopolysaccharide factor (ALF), a family of AMPs, identified from Marsupenaeus japonicus. We report the identification of LBDA-D as an output of this algorithm with the input of the original LBDMj sequence and show the NMR solution structure of LBDB, which possesses a circular extended structure with a disulfide crosslink in each terminus and two 310-helices and exhibits a broad antimicrobial spectrum. Scanning electron microscopy and transmission electron microscopy showed LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. The co-injection of LBDB and Vibrio alginolyticus, Staphylococcus aureus and another major pathogen in shrimp aquaculture white spot syndrome virus in vivo improved the survival of M. japonicus, indicating a promising therapeutic role of LBDB for infectious disease. The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.