Optimization of the antimicrobial peptide Bac7 by deep mutational scanning

Area of Science:

• Microbiology
• Drug Discovery
• Biotechnology

Background:

• Intracellularly active antimicrobial peptides offer potential for new antibiotics but face challenges in drug development due to vast sequence spaces.
• Developing novel antimicrobial agents is crucial to combat rising antimicrobial resistance.

Purpose of the Study:

• To create a high-throughput platform for investigating peptide sequence-activity relationships and optimizing antimicrobial activity.
• To identify and develop novel antimicrobial peptide drug candidates.

Main Methods:

• Developed a platform using deep mutational scanning of DNA-encoded peptides and bacterial self-screening with next-generation sequencing.
• Applied the platform to >600,000 variants of the proline-rich antimicrobial peptide Bac7_1-23.
• Designed a focused library and identified a lead candidate, Bac7PS.

Main Results:

• Identified Bac7PS, a potent antimicrobial peptide effective against multidrug-resistant *E. coli* clinical isolates.
• Bac7PS demonstrated reduced dependence on the SbmA transporter and strong ribosomal inhibition.
• Bac7PS showed low eukaryotic cell toxicity and efficacy in a murine septicemia model.

Conclusions:

• The developed platform enables efficient establishment of sequence-activity relationships for antimicrobial peptides.
• The platform is valuable for hit-to-lead identification and optimization of antimicrobial drug candidates.
• This approach accelerates the development of novel peptide-based antibiotics.