结构导向的泛特异性T细胞受体--主要基因相容性复杂相互作用预测.
Letao Gao1, Yumeng Zhang2, Fang Ge3
1School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China.
Journal of chemical information and modeling
|April 29, 2025
概括
我们开发了计算模型来预测T细胞受体 (TCR) 与-MHC复合体的相互作用,进步了我们对适应性免疫的理解. 这些模型准确地预测了绑定和接触点,为TCR识别机制提供了新的见解.
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:
- T细胞受体 (TCRs) 对于适应性免疫至关重要,它们能识别-MHC复合体 (pMHCs).
- 了解TCR-pMHC相互作用对于免疫学和药物开发至关重要.
- 数据驱动的计算方法为研究这些相互作用提供了有希望的途径.
研究的目的:
- 开发和验证用于预测TCR-pMHC结合特异性和接触点的新型计算框架.
- 研究TCR-pMHC相互作用的结构和序列决定因素.
- 提供适应性免疫反应的定量分析工具.
主要方法:
- 精选了人类CD8+T细胞TCR和MHC I类呈现的表位的综合序列和结构数据集.
- 开发了SG-TPMI,这是一个结构导向模型,用于预测TCR-pMHC结合.
- 开发了Seq/Struct-TCS,序列和基于结构的模型来预测TCR-pMHC接触点.
- 将集成的MHC-Iα螺旋和结构复杂信息集成到预测模型中.
主要成果:
- SG-TPMI和Struct-TCS的性能与最先进的方法相美.
- 确定了CDR1和CDR2循环和MHC限制在TCR-pMHC相互作用中的重要性.
- 提供了对TCR-pMHC识别机制的定量见解.
结论:
- 提出SG-TPMI作为预测TCR-pMHC二进制相互作用的有效工具.
- 引入了Seq/Struct-TCS,用于预测TCR与或MHCα螺旋体相互作用的位点.
- 突出了影响TCR识别的关键结构特征,推进了免疫信息学领域.
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