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用变压器确定T细胞受体的表位特异性

Abdul Rehman Khan¹, Marcel J T Reinders^1,2, Indu Khatri^2,3

¹Department of Intelligent Systems, Delft University of Technology, Delft 2600 GA, The Netherlands.

Bioinformatics (Oxford, England)

|October 17, 2023

概括

由于TCR的多样性,预测T细胞受体 (TCR) 和表皮质结合是具有挑战性的. 深度学习方法的变压器在预测TCR-表位相互作用方面表现出卓越的表现,优于现有方法.

科学领域:

免疫学免疫学免疫学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

T细胞受体 (TCRs) 对于适应性免疫至关重要,识别由MHC分子呈现的病原体或癌症衍生的表位.
由于TCR的高度多样性和复杂的结合机制,在准确预测TCR-表皮质相互作用方面存在重大挑战.
现有的计算方法很难捕捉TCR-Epitope结合特异性的细微差别.

研究的目的:

评估变压器深度学习模型对预测TCR-epitope结合特异性的有用性.
在这个任务中比较不同预训练的变压器架构 (ProtBERT,ProtAlbert,ProtElectra,ProtXLNet) 的性能.
研究结合TCR基因使用特征对预测准确性的影响.

主要方法:

使用了四种预训练过的变压器模型:ProtBERT,ProtAlbert,ProtElectra和ProtXLNet.
这些模型在VDJdb数据库上进行了微调,其中包含25对人类和小鼠TCR-Epitope对.
实施了修改,将TCR基因使用信息纳入变压器模型.
与已建立的方法 (如TCRGP,TCRdist和DeepTCR) 进行性能比较.

主要成果:

变压器模型,特别是修改后的 ProtXLNet 包含基因使用,显著优于现有方法.
表现最好的模型在所有25个表征物中获得了0.55的加权F1得分.
基本变压器实现表现出卓越的性能,特别是在难以分类的TCR-Epitope对中.

结论:

利用注意力机制的变压器模型对于预测TCR-Epitope结合特异性非常有效.
结合TCR基因使用等生物特征,可以进一步提高这些深度学习模型的预测能力.
这种方法为推进T细胞研究和了解免疫反应提供了一个有希望的途径.

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