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Antibody Structure01:10

Antibody Structure

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Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
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Antibody Structure and Classes01:25

Antibody Structure and Classes

861
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
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Cross-reactivity00:42

Cross-reactivity

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相关实验视频

Updated: Jun 20, 2025

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

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深度学习的抗体表位用定位变换向量使用抗体表位.

Ioannis Vardaxis1, Boris Simovski1, Irantzu Anzar1

  • 1NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway.

Computational and structural biotechnology journal
|July 22, 2024
PubMed
概括

这项研究引入了一种新的方法,用于预测B细胞表位,使用二进制定位变换向量和3D蛋白质结构. 这种方法显著提高了疫苗和免疫诊断开发的预测准确性.

关键词:
抗体表位预测的预测.人工智能的人工智能是人工智能.预测B细胞表位预测探测出了皮质层的发现.免疫信息学是一种信息学.

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相关实验视频

Last Updated: Jun 20, 2025

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

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科学领域:

  • 免疫信息学是指免疫信息学.
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 准确的B细胞表位的计算预测对于疫苗和免疫诊断设计至关重要.
  • 目前的B细胞表位预测工具表现不佳,需要改进策略.

研究的目的:

  • 开发一种用于增强B细胞表位预测的新型计算方法.
  • 通过结合结构和位置信息来克服现有的表位预测方法的局限性.

主要方法:

  • 位被编码为二进制的定位变换向量,代表氨基酸属性.
  • 使用未结合蛋白质的3D宏观结构特征.
  • 深度学习模型在未结合和抗体结合的蛋白质结构上进行训练,以捕获结合启动特征.
  • 这种方法消除了培训数据中的"诱导适应"偏见.

主要成果:

  • 与现有的工具相比,这种新的策略在B细胞表位预测中显示出更高的准确性.
  • 该方法可靠地识别了SARS-CoV-2尖端蛋白的实验验证表位.
  • 该方法在未见的和不相似的数据集上显示了强大的概括.

结论:

  • 一个主要的蛋白质序列和一个查询定位位向量足以可靠地预测B细胞表位.
  • 这种方法有可能在生物医学研究中推进计算式B细胞表位预测.