PPAP:一种蛋白质-蛋白质亲和预测器,包含界面接触意识注意力
Jie Qian1, Lin Yang1, Zhen Duan2
1Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
Journal of chemical information and modeling
|September 19, 2025
概括
我们开发了PPAP,这是一种新的深度学习方法,用于预测蛋白质与蛋白质相互作用的结合亲和力. PPAP利用结构和序列数据,大大提高了蛋白质设计和相互作用研究的预测准确性.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 机器学习 机器学习
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物功能和药物发现至关重要.
- 准确预测PPI结合亲和力对于理解分子机制和蛋白质工程至关重要.
- 最近在蛋白质结构预测方面的进展为基于结构的亲和力预测提供了新的途径.
研究的目的:
- 开发一种新的深度学习框架,PPAP,用于更好地预测蛋白质与蛋白质相互作用的结合亲和力.
- 将结构信息与基于序列的特征集成,以便更准确地预测亲和力.
- 改进主要依赖序列数据的现有方法.
主要方法:
- 开发了PPAP,这是一个集成结构和序列特征的深度学习框架.
- 采用了接口接触意识的注意力机制来捕捉交互界面的细节.
- 在内部和外部测试数据集上评估模型性能.
主要成果:
- 与基于序列的方法相比,PPAP显示出更高的预测性能.
- 在内部测试组中获得了0.540的Pearson相关系数 (R) 和1.546的MAE.
- 在外部测试组中表现优于基准模型,R值高于0.63.
- 结合PPAP预测,增强了蛋白质结合剂设计的丰富度,高达10倍.
结论:
- PPAP提供了一种强大而准确的方法来预测蛋白质与蛋白质相互作用的结合亲和力.
- 该框架有效地利用来自蛋白质复合体的结构见解.
- 在蛋白质设计和其他与蛋白质相互作用相关的研究中,PPAP具有很大的应用潜力.
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