解码人类T细胞谱的特异性的定量方法
Zahra S Ghoreyshi1, Jason T George1,2,3
1Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Frontiers in immunology
|October 2, 2023
概括
预测T细胞受体 (TCR) - - 主性基因相容性复合体 (pMHC) 相互作用是免疫疗法的关键. 本综述探讨了用于识别TCR-pMHC识别对及其预测性能的计算和深度学习方法.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 对于适应性免疫来说,T细胞受体 (TCR) - - 主性基因相容性复合体 (pMHC) 相互作用是基本的.
- 了解TCR-pMHC特异性对于开发有效的免疫疗法至关重要.
研究的目的:
- 审查和分析目前用于预测TCR-pMHC相互作用的计算和深度学习方法.
- 评估现有模型的数学基础,预测准确性和局限性.
主要方法:
- 检查理论,计算和深度学习模型.
- 分析利用蛋白序列,结构或组合数据的方法.
- 基于统计学学习的方法的评估.
主要成果:
- 高通量数据使得TCR-pMHC相互作用的先进计算预测成为可能.
- 不同的模型利用序列,结构或集成数据与不同的统计学习技术.
- 性能和限制在不同的计算策略中各不相同.
结论:
- 计算方法对于破译TCR-pMHC识别越来越重要.
- 需要进一步开发,以完善预测准确性,并解决治疗应用的模型限制.
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