具有已知的抗原特异性的T细胞受体的大规模基于模板的结构建模揭示了互补性特征
Dmitrii S Shcherbinin1,2, Vadim K Karnaukhov3, Ivan V Zvyagin4
1Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
Frontiers in immunology
|August 31, 2023
概括
这项研究模拟了T细胞受体 (TCR) - -主要基因相容复合体 (MHC) 相互作用,以预测TCR特异性. 开发的方法产生各种抗原的高质量的TCR--MHC模型,有助于理解免疫反应.
科学领域:
- 免疫学 免疫学 免疫学
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 对-MHC复合体的T细胞受体 (TCR) 识别对于适应性免疫至关重要.
- 对于TCR--MHC复合体的结构数据有限,这阻碍了对抗原识别的充分理解.
- 现有的TCR序列数据库广泛,但结构信息稀少.
研究的目的:
- 以计算方式建模T细胞受体 (TCR) - - 主性基因相容性复合体 (MHC) 复合体.
- 分析控制TCR--MHC相互作用和抗原识别的结构特征.
- 开发基于序列和结构数据的TCR特异性的预测方法.
主要方法:
- 使用VDJdb数据库查找TCR序列和已知的特异性.
- 采用基于模板的建模与罗塞塔应用程序来构建TCR--MHC复杂结构.
- 使用R.进行了接触残留物和物理化学性质的统计分析.
主要成果:
- 创建了一个1585个模拟TCR--MHC结构的数据库,包括SARS-CoV-2抗原复合体.
- 分析了TCR-接口上的氨基酸接触,以确定抗原识别的决定因素.
- 根据接触残留物的物理化学性质来解释相互作用偏好.
结论:
- 为高质量的TCR--MHC建模开发了一种强大的方法.
- 这些模型可以预测各种感兴趣的抗原的TCR特异性.
- 这种方法提高了对T细胞介导的免疫反应的理解.
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