从主要结构序列推断的全球密度残留过渡图允许通过定向图卷积神经网络预测蛋白质相互作用
Islam Akef Ebeid1, Haoteng Tang2, Pengfei Gu2
1The Division of Computer Science, Texas Woman's University, Denton, TX, United States.
Frontiers in bioinformatics
|November 7, 2025
概括
本研究介绍了ProtGram-DirectGCN,这是一种用于预测蛋白质与蛋白质相互作用 (PPI) 的新型图形表示学习框架. 这种计算效率高的方法为推进药物开发的资源密集型模型提供了有力的替代方案.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 准确预测蛋白质与蛋白质相互作用 (PPI) 对理解细胞机制和药物发现至关重要.
- 目前的in-silico方法通常依赖于计算密集的方法,如蛋白语言模型 (PLMs) 或图形神经网络 (GNNs) 在3D结构上.
研究的目的:
- 调查用于预测蛋白质与蛋白质相互作用 (PPI) 的计算较少密集的替代方案.
- 通过链接预测引入和评估一种新的两阶段图表表示学习框架,用于PPI预测.
主要方法:
- 开发了ProtGram来建模蛋白质初级结构作为全球推断的n-gram图形的层次结构,其残留过渡概率作为边缘权重.
- 建议DirectGCN,一个定制的定向图形卷积神经网络,具有专门的卷积层和可学习的门机制.
- 应用DirectGCN到ProtGram图表以学习残留物和蛋白质水平嵌入,使用PPI预测的注意力机制.
主要成果:
- 在一般节点分类基准上,DirectGCN表现出与既有方法相匹配的性能,在复杂,定向和密集的异构图上表现出色.
- 完整的ProtGram-DirectGCN框架实现了强大的PPI预测预测能力,即使在有限的数据上进行训练.
结论:
- 全球推断的,指向的基于图表的序列过渡表示,为PPI预测提供了一个计算上独特和强大的替代方案,而不是资源密集型PLM.
- "ProtGram-DirectGCN"框架显示出了超越PPI预测的各种生物信息任务的前景.
关键词:
生物电网是一个生物电网.图形卷积网络是指图形卷积网络.图形神经网络的神经网络图表表示学习学习学习图表表示学习图形理论中的图形理论.链接预测 链接预测罗素的标签 罗素的标签这就是Uniprot的特点.更多相关视频
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