通过距离分析评估TCR结合预测器的概括能力
Leonardo V Castorina1,2, Filippo Grazioli2, Pierre Machart2
1School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
PloS one
|May 20, 2025
概括
改善用于预测T细胞受体 (TCR) 和结主要基因相容性复合体 (pMHC) 相互作用的机器学习模型是免疫治疗的关键. 本研究引入了一种基于距离的分割算法,以更好地评估模型对未见的概括,突出显示3D结构的重要性.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- T细胞受体 (TCR) 和与结合的主要基因相容性复合体 (pMHC) 相互作用对免疫反应和免疫治疗的发展至关重要.
- 目前的机器学习 (ML) 模型擅长预测TCR-pMHC对训练数据的结合,但在将其推广到新型上却存在困难,从而限制了治疗应用.
研究的目的:
- 评估培训和测试分布之间的距离如何影响ML模型在预测TCR-pMHC结合中的性能.
- 引入和利用一个新的距离分割 (DS) 算法,以便对模型概括能力进行更强大的评估.
主要方法:
- 评估了基于序列和基于3D结构的距离指标对ML模型概括的影响.
- 采用了包括注意变量信息瓶 (AVIB),NetTCR-2.0/2.2和ERGO II变体在内的最先进的模型.
- 介绍了距离分割 (DS) 算法,用于控制培训和测试集中的分布距离.
主要成果:
- 训练和测试之间的较低的3D形状相似性与更具挑战性的分布外任务相关,表明概括性较差.
- 基于序列的相似性显示了相反的趋势,这表明对概括有不同的影响.
- 这些发现强调了基于距离的分割对于对TCR-pMHC结合预测因子进行基准测试的实用性.
结论:
- 基于距离的分割方法,特别是考虑到3D结构相似性,对于准确评估TCR-pMHC绑定预测模型的概括至关重要.
- 这种方法可以根据它们与训练数据的不相似性,为新的预测提供信心评分.
- 将3D结构信息与序列数据相结合,可以提高TCR-pMHC结合模型的预测准确性.
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