基于生物物理学的蛋白质语言模型用于蛋白质工程
Sam Gelman1,2, Bryce Johnson1,2, Chase Freschlin3
1Department of Computer Sciences, University of Wisconsin-Madison.
bioRxiv : the preprint server for biology
|April 1, 2024
概括
突变效应转移学习 (METL) 集成了生物物理学和机器学习,用于蛋白质工程. 这种新的框架通过利用生物物理模拟来提高对蛋白质特性的预测,即使数据有限.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 蛋白质语言模型 (PLMs) 擅长使用进化数据预测蛋白质序列,结构和功能.
- 现有的PLM往往忽视了控制蛋白质行为和功能的关键生物物理因素.
研究的目的:
- 引入突变效应转移学习 (METL),这是一个新的框架,将机器学习与生物物理建模相结合.
- 通过结合生物物理原理来增强蛋白质语言模型的预测能力.
主要方法:
- 在生物物理模拟数据上预训练基于变压器的神经网络,以学习序列结构-能量学关系.
- 在实验序列功能数据上完成METL,以预测蛋白质特性,如热稳定性,活性和光.
主要成果:
- 在蛋白质工程任务中,METL表现出卓越的性能,特别是从小型数据集和位置推断中进行概括.
- 该框架成功设计了功能绿色光蛋白变体,仅使用64个训练示例.
结论:
- METL为蛋白质语言建模提供了一种强大的生物物理学知情方法.
- 这一框架显示了通过整合机器学习和生物物理见解来推进蛋白质工程和设计的巨大潜力.
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