拉索ESM是一个定制的语言模型,用于增强的拉索特性预测
Xuenan Mi1, Susanna E Barrett2,3, Douglas A Mitchell4,5
1Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Nature communications
|September 29, 2025
概括
我们开发了LassoESM,一种语言模型,可以准确预测拉索的特性. 这种工具有助于发现新的拉索,并为特定功能设计它们.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 自然产品 化学 化学
背景情况:
- 核糖体合成和翻译后改性 (RiPPs) 代表了多样化的自然产品类别.
- 拉索是一种RiPPs的子类,具有独特的[1]rotaxane结构,由拉索环酶酶稳定,从而产生多种生物活性和稳定性.
- 由于数据有限和复杂的健身景观,难以预测拉索的特性,例如基质兼容性和生物活性.
研究的目的:
- 开发LassoESM,一种旨在增强拉索性质预测的语言模型.
- 为了利用 LassoESM 预测基质与特定 lasso 循环的兼容性.
- 改进新型非同源环酶基质对的识别,并预测RNA聚合酶抑制活性.
主要方法:
- 开发LassoESM,一个专门针对激光的语言模型.
- 使用LassoESM嵌入式来预测基质兼容性和识别新型环酶-基质相互作用.
- 应用该模型来预测拉索中的RNA聚合酶抑制活性.
主要成果:
- 拉索ESM嵌入器准确地预测了拉索循环的基质兼容性.
- 该模型有助于发现新的非同源环酶-基质对.
- 拉索ESM增强了对拉索的RNA聚合酶抑制活性的预测.
结论:
- 拉索ESM显著改善了拉索特性的预测.
- 该模型是合理设计和发现新型拉索的宝贵工具.
- 预计未来的LassoESM代将进一步推进拉索的工程和探索.
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