预测突变诱导的蛋白质稳定性变化,基于通过自我监督的方法学习的几何表示
Shan Shan Li1,2, Zhao Ming Liu2,3, Jiao Li1,2
1High Performance Computing Center, National Vaccine and Serum Institute (NVSI), Beijing, China.
BMC bioinformatics
|August 28, 2024
概括
我们开发了mutDDG-SSM,这是一个深度学习框架,可以预测突变导致的蛋白质稳定性变化. 这个工具准确地估计了突变效应,有助于蛋白质工程和药物设计.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习 机器学习
背景情况:
- 蛋白质的热稳定性对于生物功能至关重要.
- 预测突变诱导的稳定性变化对于理解蛋白质结构功能关系至关重要.
- 准确的预测有助于蛋白质工程和制药设计.
研究的目的:
- 介绍 mutDDG-SSM,一个深度学习框架,用于预测突变诱导的蛋白质稳定性变化.
- 利用几何蛋白质结构表示来提高预测准确度.
- 为蛋白质工程和药物设计提供一种有价值的工具.
主要方法:
- 开发了mutDDG-SSM,这是一个两部分的深度学习框架.
- 利用图表注意力网络与自我监督学习进行结构特征提取.
- 采用了极端梯度提升模型来预测稳定性变化,以减轻过度装配.
主要成果:
- mutDDG-SSM在独立数据集中预测突变对蛋白质稳定性的影响方面表现出很高的表现.
- 关于肌球蛋白和p53的案例研究验证了该模型的有效性.
- 该模型显示出良好的公正性,对直接和反向突变的准确性相似.
结论:
- 预训练模型为下游任务提供了有意义的特征.
- mutDDG-SSM 作为蛋白质工程和药物设计的宝贵工具.
- 该框架准确地预测了突变诱导的蛋白质稳定性变化.
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