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EPIC-TRACE:使用注意力和上下文嵌入来预测TCR与未见的表位结合

Dani Korpela¹, Emmi Jokinen^1,2,3, Alexandru Dumitrescu¹

¹Department of Computer Science, Aalto University, 02150 Espoo, Finland.

Bioinformatics (Oxford, England)

|December 9, 2023

概括

一个新的机器学习模型预测T细胞受体 (TCR) 和-MHC (pMHC) 相互作用,改善了适应性免疫和自身免疫疾病研究的概括性.

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细胞对抗病原体和癌症的适应性免疫是至关重要的,但异常的T细胞反应会导致自身免疫性疾病.
对-MHC (pMHC) 复合体的T细胞受体 (TCR) 识别对于启动免疫反应至关重要.
预测TCR-pMHC相互作用对于了解免疫功能和疾病至关重要,但目前的模型面临着概括性的挑战.

研究的目的:

开发一种先进的机器学习模型,用于预测TCR-pMHC相互作用.
增强TCR-pMHC结合的预测模型的概括能力.
研究个体特征和数据增强策略在TCR-pMHC预测中的贡献.

主要方法:

使用ProtBERT嵌入用于TCRα和β链的氨基酸序列和表位.
采用卷积和多头注意力架构来建模复杂的相互作用.
将具有有限TCR的表位数据纳入训练中,以提高模型的稳定性.

主要成果:

开发的模型在预测TCR-pMHC相互作用方面表现强.
特性重要性分析突出了TCR链,表位和MHC信息的重要性.
包括稀疏的表位数据改善了与现有的最先进方法相比的模型概括.

结论:

新型机器学习方法有效预测TCR-pMHC相互作用,优于当前的模型.
该模型对未见的pMHCs进行概括的能力为免疫学研究提供了重大潜力.
这项工作为研究适应性免疫,癌症免疫学和自身免疫性疾病提供了宝贵的工具.

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