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相关概念视频

Antibody Structure01:10

Antibody Structure

59.8K
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...
59.8K
Antibody Structure and Classes01:25

Antibody Structure and Classes

871
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.
871
Affinity and Avidity01:41

Affinity and Avidity

35.9K
Overview
35.9K
Hybridoma Technology01:31

Hybridoma Technology

14.4K
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,...
14.4K
Antibody Actions01:26

Antibody Actions

1.1K
Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
Neutralization
Antibodies can bind to pathogens, preventing them from infecting host cells. This process...
1.1K

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相关实验视频

Updated: Jun 22, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

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使用深度学习的抗体设计:从序列和结构设计到亲和力成熟.

Sara Joubbi1,2, Alessio Micheli1, Paolo Milazzo1

  • 1Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy.

Briefings in bioinformatics
|July 3, 2024
PubMed
概括
此摘要是机器生成的。

深度学习通过整合计算和实验方法来加速抗体发现. 这种方法简化了针对复杂标的治疗抗体的开发.

关键词:
抗体是一种抗体.抗体设计 抗体设计抗体优化优化 抗体优化深度学习是一种深度学习.纳米体是一个纳米体.

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Generation of Murine Monoclonal Antibodies by Hybridoma Technology
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Generation of Murine Monoclonal Antibodies by Hybridoma Technology

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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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相关实验视频

Last Updated: Jun 22, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

12.4K
Generation of Murine Monoclonal Antibodies by Hybridoma Technology
09:42

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

41.7K
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

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

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科学领域:

  • 计算生物学 计算生物学
  • 生物技术是生物技术.
  • 药物发现 药物发现 药物发现

背景情况:

  • 深度学习在计算机视觉和自然语言处理方面表现出色,在生物学中提供了强大的应用.
  • 传统的药物开发使用深度学习专注于小分子.
  • 最近的进展将深度学习融入到生物分子的发现中,特别是抗体.

研究的目的:

  • 调查用于抗体设计和优化深度学习的进展.
  • 突出了简化抗体开发的计算技术.
  • 涵盖蛋白质设计,折叠,对接和抗体的亲和力成熟.

主要方法:

  • 在 silico 和 in vitro 方法的整合用于抗体的发展.
  • 用于候选人生成和扩展的计算能力.
  • 蛋白质设计和优化技术的分析.

主要成果:

  • 深度学习显著增强了抗体发现和开发过程.
  • 计算方法加快了对抗体候选人的识别.
  • 新技术简化了复杂抗原的抗体工程.

结论:

  • 深度学习是现代抗体发现的变革性工具.
  • 整合计算和实验方法可以加速治疗抗体的开发.
  • 这项调查提供了对前沿抗体设计和优化策略的见解.