MutPPI+:通过基于突变路径的数据增强来预测蛋白质-蛋白质相互作用的突变影响的多式框架
Juntao Deng1, Miao Gu1, Pengyan Zhang1
1Department of Automation, Tsinghua University, Shuangqing Road 30, Haidian District, Beijing, 100084, China.
Briefings in bioinformatics
|March 8, 2026
概括
预测突变如何影响蛋白质与蛋白质相互作用 (PPI) 对了解疾病至关重要. 新的深度学习模型MutPPI和MutPPI-plus准确地预测了这些稳定性变化 (ΔΔG),有助于蛋白质工程.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是细胞过程的基础.
- PPI的失调与许多疾病有关.
- 对PPI稳定性 (ΔΔG) 突变影响的准确预测对于阐明疾病机制和蛋白质工程至关重要.
研究的目的:
- 开发和验证先进的计算模型,用于预测突变对蛋白质-蛋白质相互作用稳定性的影响.
- 通过整合结构,进化和多模式数据来提高预测准确性.
- 引入一种新的数据增强策略,以改善模型通用化.
主要方法:
- 开发 MutPPI,一个基于图形的深度学习模型,利用GIN-GAT从蛋白质复杂结构中提取特征.
- 从蛋白质语言模型中整合进化信息以创建MutPPI-plus.
- 实施基于突变路径的数据增强策略,以丰富输入模式.
- 对基准数据集的性能评估,包括单点 (S645) 和多点突变数据集 (SM_ZEMu,SM595,SM1124).
主要成果:
- 在S645数据集上,MutPPI在12种现有方法中表现出优越的性能.
- 结合进化信息的MutPPI-plus实现了更高的预测准确度.
- 突变路径数据增强策略改善了这两种模型的概括性.
- 使用数据增强的 MutPPI-plus 在多个数据集上取得了最先进的结果,显著超过了 DDMut-PPI.
结论:
- 开发的多式联机深度学习模型 (MutPPI和MutPPI-plus) 为预测ΔΔG提供了多功能和准确的计算工具.
- 物理信息化的数据增强方法增强了模型的概括性和预测能力.
- 这些进展促进了合理的蛋白质设计,并加深了对疾病背景中的突变效应的理解.
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