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相关概念视频

Cross-reactivity00:42

Cross-reactivity

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Antibody Structure01:10

Antibody Structure

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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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Hybridoma Technology01:31

Hybridoma Technology

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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...
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相关实验视频

Updated: Jul 18, 2025

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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抗体中最近的进展 预测皮层的抗体

Xincheng Zeng1, Ganggang Bai1, Chuance Sun1

  • 1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.

Antibodies (Basel, Switzerland)
|August 22, 2023
PubMed
概括

皮层预测促进了疫苗和治疗开发. 改善形状表位预测准确度对于实际应用至关重要,深度学习显示出有前途.

科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 疫苗学 疫苗学 疫苗学

背景情况:

  • 在开发疫苗和疗法方面,预测皮层至关重要.
  • 目前的方法需要显著提高准确性,特别是对形状表征的表征.
  • 了解抗体-抗原识别机制是关键.

研究的目的:

  • 审查抗体-抗原识别,专注于构造性表位预测.
  • 分析当前表位预测策略的成功和局限性.
  • 探索深度学习和基于特征的方法的作用.

主要方法:

  • 对抗体-抗原识别机制的文献综述.
  • 在抗体-抗原结合中对形状选择的分析.
  • 对深度学习和传统机器学习模型进行评估,以进行表位预测.

主要成果:

  • 深度学习模型显示了比传统方法更好的性能.
  • 序列和结构特征对于表位预测仍然具有信息意义.
  • 符合性表位预测带来了独特的挑战.

结论:

关键词:
抗体是一种抗体.抗体设计 抗体设计人工智能的人工智能是人工智能.标志性 标志性 标志性 标志性

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  • 临床翻译需要在表位预测方面取得进一步的进步.
  • 整合各种功能和先进的算法可以提高预测准确度.
  • 符合性表位预测仍然是未来研究的关键领域.