ProtGeoNet-Pocket:一种结合性站点预测方法,集成序列,几何和图形结构
Mingjian Jiang1, Zhi Zhang1, Teng Ma1
1School of Information and Control Engineering, Qingdao University of Technology, No. 777 Jialingjiang East Road, Huangdao District, Qingdao, Shandong 266520, China.
Journal of chemical information and modeling
|September 17, 2025
概括
通过整合序列,几何和图形数据,ProtGeoNet-Pocket准确地预测了蛋白质结合位置. 这种多式模式框架实现了高精度,在蛋白质结构分析中表现优于现有的方法.
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:
- 蛋白质结合部位的识别对于理解生物功能和药物发现至关重要.
- 现有的方法经常与复杂的3D结构和各种形状的蛋白质结合口袋作斗争.
- 整合多模式数据 (序列,几何,图形) 为提高预测准确性提供了一个有希望的途径.
研究的目的:
- 开发一个创新的多式预测框架,ProtGeoNet-Pocket,用于增强蛋白质结合位点识别.
- 利用多个规模的结构信息来解决蛋白质结构复杂性和结合口袋形状多样性.
- 通过有效的功能集成和先进的网络架构实现高精度的绑定站点预测.
主要方法:
- 利用PointNet模块从残留坐标中提取几何特征,并通过注意力机制进行增强.
- 融合的几何特征与编码的序列信息和图形边缘特征.
- 采用了带有传递信息机制的图形同态网络 (GIN) 来捕获拓关系.
主要成果:
- 在scPDB训练组中获得72.87%的F1分数.
- 在五个独立的基准数据集 (COACH420,HOLO4K,SC6K,PDBbind,ApoHolo) 中表现出强大的预测性能.
- 可视化证实了预测和实际结合地点之间的高空间重叠,表明性能优越.
结论:
- ProtGeoNet-Pocket有效地整合了多式联运数据,以准确地预测蛋白质结合地点.
- 与现有方法相比,该框架显示出更高的性能.
- 这种方法在推动药物发现和蛋白质功能研究方面具有重大潜力.
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