用蛋白质语言模型进行TCR表示学习:综合回顾
Kyohei Kinoshita1, Tetsuya J Kobayashi2,3,1
1University of Tokyo Graduate School of Engineering, Department of Electrical Engineering and Information Systems, Bunkyo, Tokyo, Japan.
International immunology
|August 26, 2025
概括
蛋白语言模型 (PLM) 提供了一种强大的新方法来分析T细胞受体 (TCR) 谱,改善免疫分析和疾病诊断. 这些模型克服了传统方法的局限性,可以更好地预测TCR功能.
科学领域:
- 免疫学
- 生物信息学
- 计算生物学
背景情况:
- T细胞受体 (TCR) 提供了对免疫状态和感染史的洞察力.
- 由于其多样性和异质性,分析TCR曲目是复杂的.
- 目前TCR分析的临床应用正在出现.
研究的目的:
- 审查蛋白质语言模型 (PLM) 的进展,以分析TCR谱.
- 将基于PLM的方法与传统的TCR数据表示和分析方法进行比较.
- 突出PLM在预测TCR抗原特异性的潜力,并使临床应用成为可能.
主要方法:
- 对T细胞受体 (TCR) 序列应用的蛋白质语言模型 (PLM) 的审查.
- 描述TCR数据表示和PLM培训的方法.
- 监督深度学习模型与基于PLM的抗原特异性预测方法的比较.
主要成果:
- PLM从大型 TCR 数据集中捕获上下文依赖的特征.
- 即使使用有限的标记数据,PLM也通过转移学习表现出高的概括性.
- 基于PLM的方法对抗原特异性预测有前途,性能优于传统方法.
结论:
- 与传统的 TCR 序列表示方法相比,PLM 具有显著的优势.
- 仍然存在一些挑战,包括数据稀缺,偏差和缺少配对链信息.
- 未来的工作应侧重于数据集优化,PLM可解释性,以及用于增强临床应用的多模式方法.
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