精确预测蛋白质的三级和四级稳定性使用微调的蛋白质语言模型和自由能量扰动
Xinning Li1, Ryann Perez1, John J Ferrie2
1Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA.
International journal of molecular sciences
|August 14, 2025
概括
蛋白质语言模型 (PLMs) 现在可以使用微调精确预测蛋白质稳定性和突变. 这些模型推进了单个蛋白质和复合体的计算蛋白质建模.
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.
- 生物物理学的生物物理.
背景情况:
- 蛋白质结构预测已经有了显著的进步,像AlphaFold.com这样的工具.
- 预测蛋白质热力学稳定性是计算蛋白质建模的一个关键挑战.
- 蛋白质语言模型 (PLM) 为了解蛋白质行为提供了一种新的方法.
研究的目的:
- 开发和验证使用PLM预测蛋白质和复合体稳定性的方法.
- 评估微调的PLM在处理大型蛋白质突变物稳定性数据集方面的能力.
- 探索PLM在预测突变的功能影响和复合体中稳定/不稳定突变中的应用.
主要方法:
- 在蛋白质突变稳定性数据集上微调 ProtT5 蛋白质语言模型.
- 利用绿色光蛋白 (GFP) 的亮度作为折叠和稳定性的功能读数.
- 应用PLM来分析影响蛋白质复合体稳定性的突变.
- 整合自由能量扰动 (FEP) 模拟来完善PLM预测.
主要成果:
- 微调的ProtT5在一个大型蛋白质突变体稳定性数据集上实现了准确的预测.
- PLM成功捕获了蛋白质功能上的突变效应,以GFP亮度为例.
- PLM在识别蛋白质复合体中稳定和不稳定突变方面表现出有效性.
- 将PLM与FEP模拟相结合,提高了预测准确度.
结论:
- PLM是预测蛋白质和复合体稳定性的多功能工具.
- 在相关数据集上微调PLM可以实现准确的定量预测.
- PLM代表了计算蛋白质建模和设计的重大进步.
相关概念视频
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