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空间层次蛋白质-蛋白质相互作用站点预测使用挤压激发囊网络

Weipeng Lv, Changkun Jiang, Jianqiang Li

IEEE transactions on computational biology and bioinformatics

|December 1, 2025

概括

MSE-CapsPPISP是一种新的深度学习模型,通过捕获蛋白质序列中的空间层次结构,准确地预测蛋白质-蛋白质相互作用 (PPI) 站点. 这种方法改进了用于识别蛋白质中关键相互作用位点的传统方法.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

蛋白与蛋白相互作用 (PPI) 是细胞过程的基础.
由于实验方法的局限性,通过计算预测PPI地点至关重要.
现有的深度学习模型往往忽略了蛋白质序列中的空间层次结构,限制了预测准确性.

研究的目的:

开发一个先进的深度学习模型,MSE-CapsPPISP,用于准确预测蛋白质-蛋白质相互作用地点.
将来自蛋白质序列的空间层次信息纳入预测模型.
为了增强蛋白质序列特征的稳定性和表示性.

主要方法:

设计了MSE-CapsPPISP,这是一个使用囊网络的深度学习模型,用于捕获蛋白质序列特征中的等级关系.
采用多级卷积神经网络 (CNN) 来提取多种特征表示.
集成的Squeeze-and-Excitation块可以改进功能重新校准.

主要成果:

与基线CNN (DeepPPISP) 和其他现有方法相比,MSE-CapsPPISP表现优越.
该模型在关键性能指标上取得了显著的改进:F1,马修斯相关系数 (MCC),接收器操作特征曲线下的区域 (AUROC) 和精度回调曲线下的区域 (AUPR).

结论:

拟议的MSE-CapsPPISP模型有效地解决了PPI站点预测中的传统深度学习方法的局限性.
通过囊网络整合空间层次结构显著提高了预测蛋白质-蛋白质相互作用地点的准确性和稳定性.

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