用几何学学习和预训策略预测蛋白质与蛋白质结合部位
Wenhong Xu1, Yuqian Xie1, Huasen Jiang1
1College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.
Journal of chemical information and modeling
|October 24, 2025
概括
通过先进的几何图形学习,GeoPPIS准确地预测了蛋白质与蛋白质相互作用的地点. 这种方法克服了现有的计算工具的局限性,为药物开发提供了可靠的in silico解决方案.
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
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要.
- 实验性识别PPI站点是资源密集的.
- 当前的计算方法在3D结构,有限的数据和性能差异方面扎.
研究的目的:
- 开发一种先进的计算方法,准确地预测蛋白质与蛋白质相互作用部位 (PPIS).
- 解决现有方法的局限性,包括不充分的结构特征建模和过度装配.
- 提高PPIS预测的稳定性和稳定性.
主要方法:
- 介绍了GeoPPIS,一种利用几何图形学习模块的几何意识方法.
- 采用了两阶段混合转移学习策略,以减轻过度装配.
- 实现基于密度的聚类和集体学习,用于预测后的改进.
主要成果:
- 在两个基准数据集 (>370种蛋白质) 上,GeoPPIS实现了最先进的性能.
- 与11个基线模型相比,实现了高预测准确度 (0.857和0.860).
- 证明了3D结构特征的卓越建模,并减少了预测方差.
结论:
- GeoPPIS提供了一个可靠的in silico工具,用于稳健的蛋白质-蛋白质相互作用地点预测.
- 结合几何信息可以提高预测的准确性和稳定性.
- 该方法促进了精准医学和药物开发的进步.
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