Alphappimi:用于预测PPI调节器相互作用的全面深度学习框架
Dayan Liu1,2, Tao Song1,2, Shuang Wang1,2
1College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China.
Journal of cheminformatics
|August 29, 2025
概括
AlphaPPIMI是一个新的深度学习框架,可以准确预测针对蛋白质-蛋白质相互作用 (PPI) 和它们的接口的调节器. 这种计算工具有助于通过优先考虑潜在的药物候选物来发现有针对性的PPI治疗方法.
科学领域:
- 计算生物学
- 药物发现
- 生物信息学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要, 它们的失调与疾病有关.
- 确定针对PPI及其接口的调节剂是关键的治疗策略.
- 传统方法难以识别PPI调节剂,特别是缺乏已知的活性化合物的标.
研究的目的:
- 开发一个深度学习框架AlphaPPIMI,用于预测蛋白质相互作用调节器 (PPIMI) 的相互作用.
- 专门针对PPI接口进行调节器发现.
- 为评估PPIMI预测方法创建可靠的基准数据集.
主要方法:
- 综合多模式分子特征 (Uni-Mol2),蛋白质表示 (ESM2,ProTrans) 和PPI结构特征 (PFeature).
- 采用专门的交叉注意力架构来融合多种分子表示.
- 利用条件域对抗网络 (CDAN) 来增强跨域的通用化.
主要成果:
- 与现有方法相比,AlphaPPIMI在PPIMI预测方面表现优异.
- 该框架有效地学习了PPI目标和调节器之间的关联.
- 在多种蛋白家族中实现了强大的跨域泛化.
结论:
- 阿尔法PPIMI提供了一个强大的计算工具来优先考虑候选PPI调节器.
- 该框架显示了针对性PPI治疗方法的发现,特别是那些对蛋白质-蛋白质接口起作用的治疗方法.
- 这项工作推进了复杂蛋白质标的计算药物发现领域.
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