ASCE-PPIS:一种基于等价图神经网络的蛋白质-蛋白质相互作用站点预测器,具有结构意识聚合和图形崩的融合
Guanghao Shen1,2, Ziqi Zhang1,2, Zhaohong Deng1,2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214012, China.
Bioinformatics (Oxford, England)
|July 24, 2025
概括
ASCE-PPIS是一种新的等价图神经网络,通过整合分子相互作用和多尺度特征来增强蛋白质与蛋白质相互作用地点的预测. 这种方法在Test60数据集上显示了超过10%的改进.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 识别蛋白质与蛋白质相互作用部位 (PPIS) 对于了解疾病和开发药物至关重要.
- 实验性PPIS识别是昂贵和耗时的,这推动了对计算方法的需求.
- 目前用于PPIS预测的图形神经网络 (GNN) 方法在利用氨基酸相互作用和处理密集蛋白质图形方面存在局限性.
研究的目的:
- 开发一种先进的计算方法,用于准确预测蛋白质与蛋白质相互作用地点.
- 通过结合分子相互作用和多尺度图形特征来解决目前基于GNN的方法的局限性.
- 改进PPIS预测模型在多种蛋白质结构中的泛化性能.
主要方法:
- 提出ASCE-PPIS,一个等价图神经网络 (GNN) 用于蛋白质-蛋白质相互作用预测.
- 集成图形聚合和图形崩,以捕捉结构适应子图的多尺度特征.
- 采用了集体策略,包括包装和元建模,并结合了蛋白质大型语言模型功能,以提高概括性.
主要成果:
- ASCE-PPIS表现出显著的性能改善,在Test60数据集上超过10%的现有方法.
- 该模型有效地学习使用等价神经网络的分子特征和相互作用.
- 多尺度特征和整体策略的整合导致了更高的预测准确度.
结论:
- ASCE-PPIS提供了一种新且有效的方法来预测蛋白质与蛋白质相互作用地点.
- 该方法利用分子相互作用和多尺度图形特性的能力推动了计算生物学的进步.
- ASCE-PPIS显示了加速药物发现和了解疾病机制的巨大潜力.
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