HGT-PepPI:一种基于异质图的框架,利用实用分析来预测-蛋白相互作用
Ke Yan1, Tianyi Liu1, Xinxin Zhan2
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Journal of chemical information and modeling
|March 17, 2026
概括
基于图形的新型框架HGT-PepPI通过整合各种生物数据,准确地预测蛋白相互作用. 这种方法提高了概括性,并有助于识别用于类药物设计的关键残留物.
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.
- 药物发现 药物发现
背景情况:
- -蛋白相互作用 (PepPIs) 对生物过程至关重要.
- 目前用于Peppi预测的深度学习方法由于数据稀缺和难以捕捉复杂的交互环境而面临限制.
研究的目的:
- 开发一个先进的计算框架,HGT-PepPI,用于预测蛋白相互作用.
- 提高PepPI预测模型的概括性能和稳定性.
主要方法:
- 使用基于异质图形的框架 (HGT-PepPI).
- 作为节点的初始化和蛋白质序列,具有来自ProtT5.5的语义表示.
- 构建了多关系边缘,整合了序列语义,进化保护和已知的相互作用.
- 用于本地和全球依赖模型的消息传递操作.
主要成果:
- 与最先进的方法相比,HGT-PepPI表现出优越的预测性能和稳定性.
- 实验验证,包括氨酸扫描突变发生和结合亲和度测试,证实了该模型识别关键残留物的能力.
- 该模型成功指导了类药物设计策略.
结论:
- 通过有效地整合多源生物信息,HGT-PepPI提供了一种强大的方法来预测蛋白相互作用.
- 该框架显示了促进类药物设计和理解分子相互作用的巨大潜力.
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