Design of multimodal antibiotics against intracellular infections using deep learning

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Design of multimodal antibiotics against intracellular infections using deep learning

Authors

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

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 while retaining 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, penetrated mammalian cells and eradicated intracellular Staphylococcus aureus. In mouse models of skin abscess and peritonitis, Turingcin reduced bacterial loads by up to two orders of magnitude. In sum, APEXduo generated multimodal antibiotics, opening new avenues for molecular design.

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