RCLG-PPIS:结构意识的蛋白质与蛋白质相互作用站点预测与级的本地和全球信息
Jing Chen1,2, Xiaobo Ge1, Qiuyao Qi1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Journal of chemical information and modeling
|February 20, 2026
概括
本研究介绍了RCLG-PPIS,这是一种通过有效地结合本地和全球结构信息来预测蛋白质-蛋白质相互作用地点的新方法. 它显著提高了预测的准确性,有助于了解细胞功能和药物开发.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习 机器学习
背景情况:
- 蛋白与蛋白相互作用部位 (PPIS) 的预测对于细胞生物学和药物发现至关重要.
- 图形神经网络 (GNN) 是有前途的,但对本地和全球信息的分离处理限制了准确性.
- 目前用于整合结构数据的融合策略需要改进.
研究的目的:
- 开发一种新的结构感知方法来预测蛋白质-蛋白质相互作用地点.
- 改善局部和全球蛋白质结构信息的整合.
- 提高PPIS的上下文意识和预测能力.
主要方法:
- 拟议的RCLG-PPIS方法使用残余级联局部-全球 (RCLG) 模块.
- 用于局部3D结构特征提取的E (n) 等价图神经网络 (EGNN).
- 集成变压器用于全球残留依赖性分析和拉普拉斯特异向量结构编码器 (LapSE) 用于节点特征.
主要成果:
- 在Test_60基准数据集上,RCLG-PPIS表现优越.
- 在关键指标方面取得了显著改善:AUPRC增长6.38%,MCC增长8.80%.
- 在蛋白质与蛋白质相互作用地点预测方面表现优于现有的最先进模型.
结论:
- 拟议的RCLG-PPIS方法有效地融合了本地和全球结构信息,以准确预测PPIS.
- 级联方法增强了上下文意识和预测能力.
- RCLG-PPIS代表了理解蛋白相互作用的计算方法的重大进步.
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