Antimicrobial peptides designed by computational analysis of proteomes.

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology
• Drug Discovery

Background:

• Antibiotic resistance is a growing global health threat.
• Antimicrobial peptides (AMPs) offer a potential solution due to their unique properties.
• Novel AMP discovery is crucial for developing new antimicrobial strategies.

Purpose of the Study:

• To identify and develop novel antimicrobial peptides (AMPs) from proteomes.
• To validate the antimicrobial and cytotoxic potential of identified peptides.
• To demonstrate the efficiency of a computational approach for AMP discovery.

Main Methods:

• Analysis of 150,450 proteins from diverse proteomes using in-house software and AI.
• Selection and rational modification of nine candidate peptide sequences.
• In vitro bioassays to evaluate antimicrobial activity against bacteria and yeast, and cytotoxicity against cancer cell lines.

Main Results:

• 14 out of 18 modified peptides exhibited antimicrobial activity (MIC < 10 µM) against at least three bacterial species.
• Seven peptides were effective against Candida albicans (MIC < 10 µM).
• Six peptides showed a therapeutic index > 20; two and eight peptides demonstrated activity against A549 and MCF-7 cancer cell lines, respectively.

Conclusions:

• The computational strategy combined with rational modification is highly efficient for discovering potent AMPs.
• The identified AMPs demonstrate significant antimicrobial activity and potential for therapeutic applications.
• Mechanism of action involves bacterial cell membrane damage.