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Microbial Cell, Vol. 12, No. 1, pp. 1 - 8; doi: 10.15698/mic2025.02.841

Karen O. Osiro1, Abel Gil-Ley2, Fabiano C. Fernandes1,3, Kamila B. S. de Oliveira2, Cesar de la Fuente-Nunez4-7, Octavio L. Franco1,2

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Molecular de-extinction has emerged as a novel strategy for studying biological molecules throughout evolutionary history. Among the myriad possibilities offered by ancient genomes and proteomes, antimicrobial peptides (AMPs) stand out as particularly promising alternatives to traditional antibiotics. Various strategies, including software tools and advanced deep learning models, have been used to mine these host defense peptides. For example, computational analysis of disulfide bond patterns has led to the identification of six previously uncharacterized β-defensins in extinct and critically endangered species. Additionally, artificial intelligence and machine learning have been utilized to uncover ancient antibiotics, revealing numerous candidates, including mammuthusin, and elephasin, which display inhibitory effects toward pathogens in vitro and in vivo. These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.

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Paving the way for new antimicrobial peptides through molecular de-extinction by Osiro et al. is licensed under a Creative Commons Attribution 4.0 International License.