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Deep-learning dive into the antimicrobial potential of Archaea
Alejandro Rodríguez-Gijón1, Rafael Laso-Pérez1
1Department of Biogeochemistry and Microbial Ecology, Museo Nacional de Ciencias Naturales, CSIC, Madrid, Spain.
Abstract:
The global burden of antimicrobial resistance demands the urgent development of new antibiotics. To face this threat, Torres et al. leveraged deep-learning models to identify archaeal encrypted peptides (termed 'archaeasins') with potential antimicrobial activity. This work highlights Archaea as an underexplored but potentially rich source of antibiotics.
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