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相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

550
Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
550
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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相关实验视频

Updated: Jun 16, 2025

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

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预测T细胞受体功能对突变表位体的功能.

Felix Drost1, Emilio Dorigatti2, Adrian Straub3

  • 1Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.

Cell genomics
|August 16, 2024
PubMed
概括
此摘要是机器生成的。

预测针对突变版本的T细胞表位特异激活 (P-TEAM) 是一种新的计算模型,可以准确预测T细胞对突变表位特异的反应. 这种工具有助于了解T细胞对抗癌细胞和逃避免疫检测的病原体的功能.

关键词:
T细胞受体受体的 T 细胞受体.预测TCR-表位的预测.积极学习是积极学习.交叉反应性 交叉反应性深度突变扫描 (deep mutational scan) 是一种深度突变扫描.标志性 标志性 标志性 标志性机器学习是机器学习.突变是一种突变.

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相关实验视频

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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 癌细胞和病原体突变表位,以逃避T细胞受体 (TCRs).
  • 在免疫疗法中,TCR交叉活性可以对抗免疫逃避,但存在自身免疫副作用的风险.
  • 预测T细胞对突变表位体的反应对于有效的癌症和传染病治疗至关重要.

研究的目的:

  • 开发一种针对突变表位体的T细胞功能预测模型.
  • 评估单点突变对T细胞受体相互作用的影响.
  • 为研究免疫治疗中T细胞反应提供计算工具.

主要方法:

  • 开发了一种基于森林的随机模型,名为"对突变版本的预测T细胞突特异激活 (P-TEAM).
  • 在三个数据集上训练并验证了P-TEAM,这些数据集涵盖了模型表位的单氨基酸突变 (SIINFEKL,VPSVWRSSL,NLVPMVATV).
  • 评估了9690个独特的TCR-表皮质相互作用的模型性能,包括未见的TCR和突变.

主要成果:

  • P-TEAM准确地分类了T细胞对突变表位体的反应性.
  • 该模型对新的单点突变和TCRs的T细胞功能进行了定量预测.
  • 在各种各样的表位数据集中,在预测T细胞反应方面表现出高准确性.

结论:

  • P-TEAM是一种有效的计算工具,用于分析T细胞对突变表位体的反应.
  • 该模型可以帮助设计更安全,更有效的基于细胞的免疫疗法.
  • 促进了对免疫规避和治疗策略中的TCR-表皮质相互作用的理解.