Osiro Karen O, Gil-Ley Abel, Fernandes Fabiano C, de Oliveira Kamila B S, de la Fuente-Nunez Cesar, Franco Octavio L
Centro de Análises Proteômicas e Bioquímicas, Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília 70790-160 Brazil.
S-Inova Biotech, Pós-graduação em Biotecnologia, Universidade Católica Dom Bosco Campo Grande, Mato Grosso do Sul Brazil.
Microb Cell. 2025 Feb 20;12:1-8. doi: 10.15698/mic2025.02.841. eCollection 2025.
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 and . These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.
分子复活已成为一种研究整个进化历史中生物分子的新策略。在古代基因组和蛋白质组提供的众多可能性中,抗菌肽(AMPs)作为传统抗生素特别有前景的替代品脱颖而出。包括软件工具和先进深度学习模型在内的各种策略已被用于挖掘这些宿主防御肽。例如,对二硫键模式的计算分析已导致在已灭绝和极度濒危物种中鉴定出六种以前未表征的β-防御素。此外,人工智能和机器学习已被用于发现古代抗生素,揭示了众多候选物,包括猛犸菌素和象菌素,它们对病原体显示出抑制作用。这些创新有望发现新型抗生素并加深我们对进化过程的理解。