通过掩面语言建模和转移学习来预测RiPP生物合成酶的基质
Joseph D Clark1, Xuenan Mi2, Douglas A Mitchell3
1School of Molecular and Cellular Biology,University of Illinois at Urbana-Champaign,Urbana, IL 61801, USA.
ArXiv
|March 11, 2024
概括
大型语言模型预测了核糖体合成和翻译后修改的 (RiPP) 酶的适应性景观. 使用这些模型进行转移学习可以提高数据效率,并为设计RiPP生物合成途径提供见解.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 合成生物学 合成生物学
背景情况:
- 核糖体合成和翻译后改性 (RiPPs) 是一类多样化的天然产品.
- 在RiPP生物合成中的酶基质乱交缺乏简单的预测规则.
- 大型语言模型 (LLM) 显示了预测适应性景观的潜力.
结论:
- 在相同的生物合成途径内,LLM可以学习酶的可转移功能形式.
- 这种方法促进了针对RiPP生物合成的基质库设计.
- 该研究强调了LLM在理解和工程复杂的酶过程中的有用性.
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