Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning

Abstract

Abstract The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2–4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.

Keywords

• Antimicrobial peptides
• Deep learning
• Transfer learning
• Cutibacterium acnes
• Pretrained protein language embedding