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相关概念视频

Cross-reactivity00:42

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Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

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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...
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Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

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An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
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相关实验视频

Updated: Jun 27, 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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使用结构和序列特征预测未见的抗原表位的TCR序列.

Hongchen Ji1, Xiang-Xu Wang1, Qiong Zhang1

  • 1Department of Oncology of Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.

Briefings in bioinformatics
|May 7, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了CATCR,这是一种使用深度学习来预测T细胞受体 (TCR) 和表皮质相互作用的计算框架. 这种方法准确地预测了未见对的结合亲和关系,改善了适应性免疫研究.

关键词:
在CATCR框架下,在TCR预测预测.卷积神经网络是一种卷积神经网络.结构特征 结构特征变压器的变压器是一个变压器.

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科学领域:

  • 计算免疫学计算免疫学
  • 生物信息学是一种生物信息学.
  • 结构生物学是结构生物学.

背景情况:

  • 对抗原的T细胞受体 (TCR) 识别对适应性免疫非常重要.
  • 大量的TCR-抗原对数据集使计算预测模型成为可能.
  • 准确预测新型TCR-抗原对的结合亲缘关系仍然是一个挑战.

研究的目的:

  • 介绍CATCR,一种用于增强预测表位和TCR相互作用的新框架.
  • 为了提高预测TCR-表皮质结合亲缘关系的准确性.

主要方法:

  • 利用卷积神经网络从残留接触矩阵 (OpenFold) 中提取特征.
  • 使用变压器编码基于段的编码序列.
  • 引入了CATCR-D (区分器),用于使用结构和序列特征进行具有约束力的评估.
  • 开发了CATCR-G (生成模块) 用于根据表位特征预测CDR3-β序列.

主要成果:

  • 在未见的表位组-TCR对上,CATCR-D实现了0.89的AUROC,表现比基准标准优于17.4%.
  • 在来自变压器评估的双向编码器表示中,CATCR-G的精度,回忆和F1得分超过95%.
  • 该框架有效地预测了未见的表位体-TCR相互作用.

结论:

  • CATCR是一种有效的工具,用于预测新型表位体-TCR相互作用.
  • 整合结构洞察力显著提高了对TCR-epitope识别的理解.
  • 使用结构和序列数据预测新型表位的TCR有望改善约束性预测.