The emergence of multidrug-resistant bacteria has led to an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum and highly effective antibacterial activity and are less prone to resistance, making them potential candidates for the next generation of antimicrobial drugs. However, screening for AMPs from a vast library of peptides through wet lab experiments is a slow and laborious process. By leveraging large datasets of labeled peptides, researchers utilize deep learning algorithms to train models that capture complex patterns and features associated with antimicrobial activity, which advance the discovery and development of novel AMPs. Since the discovery of certain lengths of AMPs has been rarely reported, we applied deep learning to accelerate the discovery of AMPs consisting of 15 amino acids and developed a model named AMPPRED15 in this article. Wet lab experiments were also conducted to evaluate the performance of the model. Fortunately, we successfully identified two AMPs, one of which demonstrated antibacterial activities comparable to the marketed antibiotic cefoperazone sodium.
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