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相关概念视频

Antigen Processing Pathways01:31

Antigen Processing Pathways

940
MHC molecules are key players in the immune response, enabling T cells to recognize and respond to specific antigens. They are present on the surface of all nucleated cells in the body and are instrumental in presenting antigens to T cells and activating them. T cells recognize the MHC-antigen complex and initiate an immune response. MHC class I and MHC class II are two main types of MHC molecules, each associated with a distinct antigen processing pathway.
MHC Class I: Presenting Endogenous...
940
Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

460
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
Complete antigens possess both immunogenicity and...
460

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相关实验视频

Updated: Jun 7, 2025

Stability and Structure of Bat Major Histocompatibility Complex Class I with Heterologous β2-Microglobulin
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排名MHC:学习排名I类-MHC结构模型.

Romanos Fasoulis1, Georgios Paliouras2, Lydia E Kavraki1,3

  • 1Department of Computer Science, Rice University, Houston, Texas 77005, United States.

Journal of chemical information and modeling
|November 18, 2024
PubMed
概括

在主要基因相容性综合体 (MHC) I 类受体中确定正确的结姿势对于疾病治疗至关重要. 我们的新方法,RankMHC,使用学习到等级准确预测从结构组合的最佳-MHC结合模式.

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:

  • 与主要基因相容性复合体 (MHC) I 类分子的结合以及随后的T细胞受体识别对于免疫应对疾病至关重要.
  • 精确识别抗原对于开发传染病和癌症治疗方法至关重要.
  • -MHC (pMHC) 结构建模越来越多地使用,但从计算组合中确定最具代表性的结姿势仍然具有挑战性.

研究的目的:

  • 为了应对通过pMHC结构建模工具产生的集合中识别正确的结模式的挑战.
  • 开发一种新的计算方法,用于在pMHC结构模型中准确排列姿势.

主要方法:

  • 酸结合姿势识别问题被定义为一个学习到等级 (LTR) 任务.
  • 一个基于LTR的新型预测器,名为RankMHC,被开发和训练来预测pMHC构造的最准确的排名.
  • RankMHC与传统的评分函数和现有的基于机器学习的预测器进行了评估.

主要成果:

  • 与经典的-联体得分函数相比,RankMHC在识别最准确的结模式方面表现出卓越的表现.
  • 开发的预测器表现优于之前基于机器学习 (ML) 的绑定姿势预测方法.
  • RankMHC被证明是多功能,适用于各种pMHC结构建模工具和协议.

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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

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结论:

  • 在准确预测pMHC复合体的结模式方面,RankMHC提供了重大进展.
  • 这种方法提高了pMHC结构建模工作流程用于抗原识别的可靠性.
  • RankMHC为推动开发基于的免疫疗法提供了有价值的工具.