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通过DNA语言模型解读元基因学的酶潜力
R Prabakaran1,2, Yana Bromberg1,2
1Department of Biology, Emory University, Atlanta, GA 30322, United States.
Nucleic acids research
|September 3, 2025
概括
我们介绍了REMME和REBEAN, 这两种语言模型用于分析微生物DNA序列. 这些模型从元基因组数据中增强了新酶和微生物功能的发现.
科学领域:
- 微生物学
- 生物信息学
- 基因组学
背景情况:
- 微生物群落执行重要的全球功能, 但它们的全部潜力在很大程度上未知.
- 甲基因组学从环境中测序微生物DNA,但分析通常依赖于与已知的序列进行比较.
- 目前的方法限制了微生物"暗物质"和新功能基因的探索.
研究的目的:
- 开发新型语言模型 (LMs) 以进行无引用的元基因学阅读分析.
- 引入REMME (阅读EMbedder用于转基因探索),以了解DNA背景.
- 介绍REBEAN (基于嵌入的酶解读器) 用于预测reads中的基因的酶功能.
主要方法:
- 开发一个预训练的基础LM,REMME,用于转基因读取嵌入.
- 在REBEAN中微调REMME以预测酶潜力和分子功能.
- 使用LM识别功能相关的基因段并注释孤儿序列.
主要成果:
- REBEAN成功地预测了酶功能,即使是以前未被描述的 (孤儿) 序列.
- 这些模型识别了与功能相关的基因部分,而没有为此任务进行明确的培训.
- 发现了新的酶,扩大了对微生物群体酶能力的理解.
结论:
- 基于语言模型的方法为无引用的元基因组分析提供了范式转变.
- REMME和REBEAN在未组装的元基因学读取中显著扩展了酶潜力的注释.
- 这些模型推进了微生物"暗物质"和功能多样性的探索.
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