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bioRxiv · 10.1101/2024.12.20.629780

Design of multimodal antibiotics against intracellular infections using deep learning

Cesaro, A.·Wan, F.·Torres, M. D. T.·de la Fuente-Nunez, C.
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Abstract

The rise of antimicrobial resistance has rendered many treatments ineffective, posing serious public health challenges. Intracellular infections are particularly difficult to treat since conventional antibiotics fail to neutralize pathogens hidden within human cells. However, designing molecules that penetrate human cells with preserved antimicrobial activity has historically been a major challenge. Here, we introduce ApexDuo, a multimodal artificial intelligence (AI) model for generating peptides with both cell- penetrating and antimicrobial properties. From a library of 50 million AI-generated compounds, we selected and characterized several candidates. Our lead, Turingcin-46, penetrated mammalian cells and reduced intracellular Staphylococcus aureus and Listeria monocytogenes. In mouse models of skin abscess and peritonitis, Turingcin-46 reduced bacterial loads by up to two orders of magnitude. In sum, ApexDuo generated multimodal peptide antibiotics, opening new avenues for designing molecules with more than one function.

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Cesaro, A., Wan, F., Torres, M. D. T., de la Fuente-Nunez, C.. 2024-12-21. Design of multimodal antibiotics against intracellular infections using deep learning. https://doi.org/10.1101/2024.12.20.629780

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