Engineering Antimicrobial Peptides via Motif Assembly for Combating Multidrug-Resistant Pathogens

Hybridization of common motifs from known antimicrobial peptides (AMPs) is a promising strategy for designing a new class of antimicrobial agents with improved activity. Herein, the machine learning-generated peptide sequence IIKLLGVLAKFVLGQ-NH₂ was utilized for sequence optimization using an alanine scan and alignment with known AMPs to identify the motifs with the highest occurrences. Motifs were then assembled to obtain positively charged peptides with 12 to 18 amino acid lengths and hydrophobicity >60%. The designed peptides exhibit broad-spectrum antimicrobial activity against multiple Gram-negative and Gram-positive bacterial strains, along with excellent pH and plasma stability. The peptides demonstrate high efficiency in eradicating bacterial biofilms with no resistance development over 300 generations against a "superbug" clinical strain of Acinetobacter baumannii. Additionally, AMPs were biocompatible and promoted fibroblast migration, which accelerated artificial wound closure. This study highlights an innovative strategy for engineering highly effective AMPs with significant potential in combating multidrug-resistant pathogens.