GRAPE:图形规范化蛋白质语言建模解锁了TCR-表位组结合特异性
Xiangzheng Fu1,2, Li Peng3, Haowen Chen4
1Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, No. 498 Shaoshan South Road, Tianxin District, Changsha, Hunan 410004, China.
Briefings in bioinformatics
|October 6, 2025
概括
通过整合图表规范化和不平衡感知学习,GRAPE增强了T细胞受体 (TCR) -表皮质结合预测. 这种新的框架通过解决当前图形神经网络模型的局限性来提高免疫疗法的准确性.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 预测T细胞受体 (TCR) -表皮质结合 (TEB) 对于开发免疫疗法至关重要.
- 使用图形神经网络 (GNN) 的现有方法在稀疏的数据和不平衡的预测中扎.
研究的目的:
- 为准确的TEB预测开发一个强大的框架,GRAPE (图形规则化的注意力蛋白嵌入).
- 为了解决TEB的GNN中的过度平滑和预测偏差.
主要方法:
- 使用蛋白质语言模型 (ESM-2) 进行进化信息的TCR/表位嵌入.
- 实现了光谱图规则化,以防止稀疏图中的过度平滑.
- 引入了动态边缘重权和可微分的AUC最大化目标,以实现不平衡弹性.
主要成果:
- 在公共TEB预测数据集上,GRAPE显著超过了最先进的方法.
- 该框架有效地减轻了过度平滑和预测偏差.
- 在预测TCR-表皮质相互作用方面表现出更好的准确性.
结论:
- GRAPE提供了一个强大而稳健的框架,用于理解TCR-表皮质相互作用.
- 这种方法在免疫学研究和新疗法设计中具有广泛的应用.
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