Design computacional e caracterização de peptídeos antimicrobianos (AMPs) contra Pseudomonas aeruginosa

Abstract

Hospital-acquired infections (HAIs) caused by Pseudomonas aeruginosa represent a significant global public health threat, especially with the emergence of multidrugresistant

strains that do not respond to any of the available therapies, including carbapenems. In this context, the development of new therapeutic approaches is essential. Antimicrobial peptides (AMPs) have emerged as a promising alternative due to their selective mechanisms of action, including interaction with the bacterial cell membrane, which complicates the development of resistance. AMPs are characterized by their structural diversity and versatile actions, exhibiting antibacterial, antifungal, antiviral, and antitumor potential. The use of computational strategies for the design and optimization of AMPs has proven effective in identifying new candidates, enabling the creation of molecules with higher specificity and lower toxicity. In this study, the TACaPe tool, built on the deep learning Transformer model, was trained to predict 100 new AMPs with antibacterial potential. To target activity against Pseudomonas aeruginosa, molecular docking analyses were performed on receptors (LasR, RhlR, and PqsR) related to the bacterium's quorum sensing pathways. The five peptides with the best computational performance were chemically synthesized and tested for antibacterial activity against the P. aeruginosa ATCC ® 27853 standard strain in vitro. The minimum inhibitory concentration (MIC) of the AMPs was determined at concentrations up to 250 µg/mL. The AMPs demonstrated anti -biofilm activity at different concentrations, with inhibition values ranging from 23% to 93.4%. Additionally, two AMPs exhibited synergistic potential with meropenem, reducing the antibiotic's MIC by up to 9.5-fold. Cytotoxicity and hemolytic activity assays indicated that the AMPs are potentially safe at concentrations necessary for antibacterial activity. These findings reinforce the utility of computational tools in the discovery and optimization of new drugs, highlighting their potential as viable alternatives for treating i nfections caused by multidrug-resistant P. aeruginosa.