一个三模块机器学习框架,用于蛋白质序列和温度依赖的β-glucosidases中的预测
Mehmet Emre Erkanli1, Yunseok Jang2, Ali Malli1
1Department of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.
ACS synthetic biology
|October 2, 2025
概括
我们开发了一个三模块的机器学习框架,使用蛋白质序列和温度来预测酶活性 (kcat/Km). 这种方法准确地模拟了依赖温度的酶功能,优于传统方法.
科学领域:
- 酶动力学和生物催化学
- 计算生物学和生物信息学
- 机器学习在生物化学中的应用
背景情况:
- 酶活性是由氨基酸序列和测试条件 (如温度) 控制的.
- 从单独的序列预测综合酶活性 (kcat/Km) 是一个挑战.
- 现有的机器学习模型难以捕捉温度依赖的酶动力学.
研究的目的:
- 开发一种机器学习框架,使用蛋白质序列和温度来预测β-葡萄糖酶kcat/Km.
- 模拟酶序列,温度和催化效率之间的复杂,非线性关系.
- 改善在不同温度下对酶活性的定量预测.
主要方法:
- 开发了一个独特的三模块机器学习框架.
- 每个模块都捕获了序列,温度和kcat/Km相互作用的不同方面.
- 集成模块用于映射β-葡萄糖酶活性的序列和温度空间.
主要成果:
- 模块化框架在未见的蛋白质序列上实现了显著的概括性能.
- 预测的温度依赖的kcat/Km值,可变性降低和过.
- 与传统的单模块机器学习方法相比,证明了更高的性能.
结论:
- 三个模块的ML框架有效地预测了温度依赖的酶活性.
- 模块化设计在模型优化和预测准确性方面提供了优势.
- 这种方法适用于其他复杂的生物系统,用于财产勘探.
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