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

Area of Science:

• Biochemistry
• Peptide Engineering
• Antimicrobial Research

Background:

• Antimicrobial peptides (AMPs) are crucial in innate immunity.
• Developing new antimicrobial agents is vital to combat rising antibiotic resistance.
• Hybridization of known AMP motifs offers a promising design strategy.

Purpose of the Study:

• To design and optimize novel antimicrobial peptides using a machine learning-generated sequence.
• To create peptides with enhanced broad-spectrum activity, stability, and biofilm eradication capabilities.
• To evaluate the therapeutic potential of engineered AMPs against multidrug-resistant pathogens.

Main Methods:

• Utilized a machine learning-generated peptide sequence for optimization via alanine scanning.
• Aligned sequences with known AMPs to identify high-occurrence motifs.
• Assembled motifs to create positively charged peptides (12-18 amino acids, >60% hydrophobicity).
• Tested antimicrobial activity against Gram-negative and Gram-positive bacteria, assessed stability, and evaluated biofilm eradication.

Main Results:

• Designed peptides demonstrated broad-spectrum antimicrobial activity.
• Achieved excellent pH and plasma stability.
• Showed high efficiency in eradicating bacterial biofilms, including against *Acinetobacter baumannii*.
• No resistance developed over 300 generations against a clinical strain.
• Peptides were biocompatible and accelerated wound closure in vitro.

Conclusions:

• An innovative strategy for engineering highly effective AMPs was developed.
• The designed peptides show significant potential for combating multidrug-resistant pathogens.
• This approach offers a promising avenue for developing next-generation antimicrobial therapies.