bioRxiv : the preprint server for biology · 2025
Design of multimodal antibiotics using deep learning.
СтатьяПрепринтНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cesaro Angela, Wan Fangping, Torres Marcelo D T, de la Fuente-Nunez Cesar
Аннотация
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.