Selection and redesign for high selectivity of membrane-active antimicrobial peptides from a dedicated...

Area of Science:

• Biochemistry and Molecular Biology
• Computational Chemistry
• Drug Discovery

Background:

• Antimicrobial peptides (AMPs) show promise as antibiotics due to low resistance development but often exhibit host cell toxicity.
• Developing selective AMPs through traditional modification is costly and inefficient.

Purpose of the Study:

• To computationally screen and design novel, selective antimicrobial peptides with reduced host cell toxicity.
• To validate the efficacy and safety of computationally designed peptides.

Main Methods:

• Modified the 'Mutator' computational tool, incorporating quantitative structure-activity relationship (QSAR) criteria, to analyze multiple peptide sequences.
• Screened the Database of Anuran Defense Peptides (DADP) to identify candidate sequences for modification.
• Synthesized and experimentally tested two novel peptide variants (Dadapins) using flow cytometry and atomic force microscopy (AFM).

Main Results:

• The modified Mutator algorithm successfully proposed 8 potentially selective AMPs (Dadapins) from the DADP.
• Tested Dadapins demonstrated potent antibacterial activity by disrupting bacterial membranes.
• Experimental validation confirmed Dadapins were non-toxic to host cells.

Conclusions:

• The enhanced computational approach effectively designs selective antimicrobial peptides with low host toxicity.
• This strategy offers a cost-effective and efficient method for developing next-generation antibiotics.