A deep-learning algorithm unravels a collection of archaeasins, peptides from the archaeal proteome with potential antimicrobial activity and implications for the development of next-generation antibiotics
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References
Antimicrobial Resistance Collaborators. Lancet 399, 629–655 (2022).
Hutchings, M. I., Truman, A. W. & Wilkinson, B. Curr. Opin. Microbiol. 51, 72–80 (2019).
Torres, M. D. T. et al. Nat. Biomed. Eng. 6, 67–75 (2022).
Meisel, H. Curr. Med. Chem. 12, 1905–1919 (2005).
Bruni, N. et al. Molecules 21, 752 (2016).
Torres, M. D. T., Wan, F. & de la Fuente-Nunez, C. Nat. Microbiol. https://doi.org/10.1038/s41564-025-02061-0 (2025).
Wan, F., Torres, M. D. T., Peng, J. & de la Fuente-Nunez, C. Nat. Biomed. Eng. 8, 854–871 (2024).
Jangra, M. et al. Nature 640, 1022–1030 (2025).
Brock, T. D. & Freeze, H. J. Bacteriol. 98, 289–297 (1969).
Staley, J. T. & Konopka, A. Annu. Rev. Microbiol. 39, 321–346 (1985).
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Laso-Pérez, R. Deep-mining the archaeal proteome for antibiotics. Nat Microbiol 10, 2365–2366 (2025). https://doi.org/10.1038/s41564-025-02098-1
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DOI: https://doi.org/10.1038/s41564-025-02098-1