Abstract
Background: The emergence and prevalence of antibiotic-resistant bacteria (ARBs) have become a serious global threat, as the morbidity and mortality associated with ARB infections are continuously rising. The activation of quorum sensing (QS) genes can promote biofilm formation, which contributes to the acquisition of drug resistance and increases virulence. Therefore, there is an urgent need to develop new antimicrobial agents to control ARB and prevent further development. Antimicrobial peptides (AMPs) are naturally occurring defense molecules in organisms known to suppress pathogens through a broad range of antimicrobial mechanisms. Methods: In this study, we utilized a previously developed deep-learning model to identify AMP candidates from the venom gland transcriptome of the spider Pardosa astrigera, followed by experimental validation. Results: PA-Win2 was among the top-scoring predicted peptides and was selected based on physiochemical features. Subsequent experimental validation demonstrated that PA-Win2 inhibits the growth of Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and multidrug-resistant P. aeruginosa (MRPA) strain CCARM 2095. The peptide exhibited strong bactericidal activity against P. aeruginosa, and MRPA CCARM 2095 through the depolarization of bacterial cytoplasmic membranes and alteration of gene expression associated with bacterial survival. In addition, PA-Win2 effectively inhibited biofilm formation and degraded pre-formed biofilms of P. aeruginosa. The gene expression study showed that the peptide treatment led to the downregulation of QS genes in the Las, Pqs, and Rhl systems. Conclusions: These findings suggest PA-Win2 as a promising drug candidate against ARB and demonstrate the potential of in silico methods in discovering functional peptides from biological data.