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相关概念视频

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

495
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...
495
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

643
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...
643

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相关实验视频

Updated: May 30, 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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TPepRet:一种深度学习模型,用于表征T细胞受体-抗原结合模式.

Meng Wang1, Wei Fan2, Tianrui Wu1

  • 1School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Bioinformatics (Oxford, England)
|January 29, 2025
PubMed
概括

通过集成后续挖掘和语义分析,TPepRet准确地解读T细胞受体 (TCR) 和结合关系. 这种新型模型的性能优于现有的工具,推进了癌症免疫疗法和疫苗设计.

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:

  • T细胞受体 (TCRs) 对于适应性免疫至关重要,识别癌症免疫疗法,疫苗设计和自身免疫性疾病管理的.
  • 准确地描述TCR-结合是必不可少的,但由于序列数据中被忽视的定向语义,在计算上具有挑战性.

研究的目的:

  • 开发一种创新的计算模型TPepRet,能够准确地解读TCR和之间的语义绑定关系.
  • 通过结合定向语义和高级序列分析来解决现有工具的局限性.

主要方法:

  • TPepRet将后续挖掘与语义集成结合在一起,使用双向门式反复单元 (BiGRU) 网络和大型语言模型框架.
  • 该模型分析了双向序列依赖性和全球序列语义,以全面了解TCR-相互作用.

主要成果:

  • TPepRet在各种数据集和具有挑战性的场景中表现出卓越的性能,包括结偏好分析和T细胞克隆扩张特征.
  • 该模型成功地在复杂的环境中识别了真结合物,评估了关键结合点,并与大规模表达数据进行了验证.
  • 评估包括查SARS-CoV-2TCRs,突出显示TPepRet的广泛适用性和准确性.

结论:

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Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
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Last Updated: May 30, 2025

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  • TPepRet显著优于现有的计算工具来分析TCR-结合.
  • 该模型为了解TCR-相互作用提供了一种强大的新方法,有可能在免疫治疗和疫苗开发中推进临床应用.