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 R Freschlin3
1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Nature methods
|September 11, 2025
概括
我们介绍了突变效应转移学习 (METL),一种新的蛋白质语言模型. METL集成了生物物理模拟,以提高蛋白质特性预测,改善蛋白质工程应用.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 机器学习 机器学习
背景情况:
- 在进化数据上训练的蛋白质语言模型 (PLM) 对于预测蛋白质序列,结构和功能是有效的.
- 现有的PLM往往忽视了控制蛋白质行为的关键生物物理因素.
- 整合生物物理原理可以增强PLM的预测能力.
研究的目的:
- 开发一种新的蛋白质语言模型框架,即突变效应转移学习 (METL),其中包括生物物理建模.
- 在生物物理模拟数据上预训练神经网络,以捕捉序列-能量关系.
- 在实验数据上微调METL,以更好地预测蛋白质特性.
主要方法:
- 开发了METL框架,将先进的机器学习与生物物理建模相结合.
- 在生物物理模拟数据上预训练过的基于变压器的神经网络.
- 在实验序列函数数据上的模型进行了微调,用于属性预测.
主要成果:
- METL有效地捕捉了蛋白质序列,结构和能量之间的基本关系.
- 该模型在蛋白质工程任务中表现出卓越的性能,包括从小型数据集进行概括和位置推断.
- METL只使用64个训练示例成功设计了功能绿色光蛋白变体.
结论:
- 通过整合生物物理见解,METL代表了蛋白质语言模型的重大进步.
- 这种基于生物物理学的方法显示了加速蛋白质工程和设计的巨大潜力.
- METL为以进化为基础的模型提供了一个强大的替代方案,特别是对于需要深入生物物理理解的任务.
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