使用图形神经网络和结构配置文件改进蛋白质-蛋白质相互作用位点预测.
Qing Zhang1, You-Hang Hu1, Yu Zhou1
1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
Analytical biochemistry
|June 30, 2025
概括
使用先进的神经网络,TargetPPI通过整合蛋白质结构和序列数据,准确地预测蛋白质与蛋白质相互作用的地点. 这种方法增强了对生物过程和蛋白质功能的理解.
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
- 结构生物学 结构生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是细胞功能的基础.
- 在PPI中识别氨基酸残留物对于理解蛋白质机制至关重要.
研究的目的:
- 开发一个准确的预测蛋白质-蛋白质相互作用站点.
- 整合蛋白质结构和序列信息,以改善预测.
主要方法:
- 利用一种新的方法,结合了卷积神经网络 (CNN),双向长期短期记忆网络 (Bi-LSTM) 和通过图表注意与节点相似性 (EGR-NS) 的边缘聚合.
- 使用CNN和Bi-LSTM分别提取了全球和本地特征.
- 整合结构特征作为边缘嵌入在蛋白质结构衍生图中.
主要成果:
- 与现有的最先进的方法相比,TargetPPI在七个独立数据集上表现出更高的性能.
- 平均准确率为84.3%,精度为57.6%,马修斯相关系数 (MCC) 为0.383.
- 平均整体策略通过整合各种模型参数来提高预测准确性.
结论:
- 目标PPI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地点.
- 结构和顺序数据的整合显著提高了预测能力.
- 开发的工具为蛋白质功能机制提供了有价值的见解,并可供公众使用.
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