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. 2025 Nov 28;136(12):lxaf287.
doi: 10.1093/jambio/lxaf287.

Novel antimicrobial peptides against Pseudomonas aeruginosa: in silico design and experimental validation

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Novel antimicrobial peptides against Pseudomonas aeruginosa: in silico design and experimental validation

Déborah Trota Farias de Albernaz et al. J Appl Microbiol. .

Abstract

Aims: This study aimed to design and evaluate novel antimicrobial peptides (AMPs) with antibacterial and antibiofilm activity against Pseudomonas aeruginosa. Computational analyses included interactions with quorum sensing (QS) receptors as potential targets involved in bacterial virulence regulation.

Methods and results: AMP sequences were generated using TACaPe, a deep learning model based on transformers, to predict peptides with antibacterial activity. The selected AMPs were assessed in silico for their ability to bind QS receptors (LasR, RhlR, and PqsR) using molecular docking analysis. The five AMPs with the highest binding affinities were chemically synthesized and tested in vitro against P. aeruginosa ATCC® 27853. Two peptides exhibited significant antibacterial effects and dose-dependent inhibition of biofilm formation. Additionally, both peptides showed synergistic activity with meropenem, lowering its minimum inhibitory concentration (MIC). Hemolytic and cytotoxic assays indicated their potential for therapeutic application.

Conclusions: Computationally designed AMPs exhibited antibacterial and antibiofilm activity against P. aeruginosa. Their synergistic effects with meropenem further enhance their therapeutic potential.

Keywords: antibiofilm; artificial intelligence; cytotoxicity; molecular docking; synergism.

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