发现暗物质:基于大型语言模型的酶动态数据提取器及其验证
Galen Wei1, Xinchun Ran1, Runeem Ai-Abssi1
1Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.
Protein science : a publication of the Protein Society
|August 15, 2025
概括
EnzyExtract是一个大型语言模型管道,自动从科学文献中提取酶动力学数据. 这解锁了大量的"暗物质"数据,显著扩大了数据集,用于改进的酶工程模型.
科学领域:
- 生物化学和生物信息学
- 计算生物学 计算生物学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 大量的酶动力学数据是无结构的,在科学文献中无法获得的.
- 这限制了对酶工程的准确预测模型的开发.
- 链接酶序列,基质,动力学和条件的关系数据基本上没有收集到.
研究的目的:
- 开发一个管道 (EnzyExtract) 来自动提取,验证和结构化来自科学文献的酶动力学数据.
- 创建一个全面的,结构化的酶动态信息数据库 (EnzyExtractDB).
- 增强可用酶数据集的规模和多样性,用于预测建模.
主要方法:
- 利用一个大型语言模型驱动的管道 (EnzyExtract) 来处理137,892个全文出版物.
- 自动提取酶-基质-动力学条目,包括kcat和Km值.
- 将提取的数据映射到酶序列 (UniProt) 和基质信息 (PubChem).
主要成果:
- 收集了超过218,095个酶基质动力学条目,显著扩大了已知的酶学数据集.
- 识别了89544个独特的动态条目,这些条目不在现有数据库中,比如BRENDA.
- 产生了92,286个高可靠性,序列映射的动态条目.
- 使用EnzyExtractDB重新训练预测模型,显示性能改善 (RMSE,MAE,R2).
结论:
- EnzyExtract成功地自动化了从文献中获取的酶动力学数据的修复.
- EnzyExtractDB提供了一个有价值的,大规模的,文学衍生数据集,用于增强酶动力学预测.
- EnzyExtract和EnzyExtractDB的开放性可用性促进了酶工程的进一步研究和开发.
相关概念视频
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