Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides

Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics.

In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus.

LBD_A-D was identified as an output of this algorithm, in which the original LBD_Mj sequence was the input, and the three-dimensional solution structure of LBD_B was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests.

The NMR solution structure showed that LBD_B possesses a circular extended structure with a disulfide crosslink at the terminus and two 3₁₀-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBD_B induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBD_B, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBD_B for treating infectious disease.

The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.