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相关概念视频

Affinity and Avidity01:41

Affinity and Avidity

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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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Antibody Structure and Classes01:25

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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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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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使用双级表示模型预测抗体-抗原亲和力.

Ziyang Wang1, Yu Zhang1, Youli Zhang2

  • 1Institute of Artificial Intelligence, School of Informatics, State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Institute of Diagnostics and Vaccine Development in Infectious Diseases, National Innovation Platform for Industry-Education Integration in Vaccine Research; and the NMPA Key Laboratory for Research and Evaluation of Infectious Disease Diagnostic Technology, Xiamen University, Xiamen, Fujian China.

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概括

DLP-Affinity是一个双层深度学习框架,使用序列数据改进了抗体-抗原亲和力预测. 它提高了各种抗体格式的准确性,即使结构信息有限.

关键词:
抗体-抗原相互作用深度学习 (Deep Learning) 是一种深度学习.蛋白质语言模型蛋白质表示学习学习学习

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 在蛋白质科学中的机器学习

背景情况:

  • 准确的抗体-抗原相互作用建模至关重要,但具有挑战性,特别是有限的结构数据.
  • 现有的基于序列的方法往往无法充分利用蛋白质序列信息.
  • 这种限制影响了各种抗体格式,包括单域抗体 (sdAbs).

研究的目的:

  • 开发一种新的深度学习框架,以准确的基于序列的抗体亲和力预测.
  • 为了解决利用不同抗体格式的序列信息的现有方法的局限性.
  • 为预测抗体-抗原结合亲缘关系提供强大的计算工具.

主要方法:

  • 提出了DLP-Affinity,一个双层深度学习框架.
  • 集成了两个模块:局部接口接触的残留到残留 (R2R) 和全球蛋白质特性的全球随机投影嵌入 (GSPE).
  • 利用精心调整的蛋白质语言模型来增强特征表示.

主要成果:

  • 在AB-Bind数据集上实现了最先进的性能,将平均绝对误差降低到20.9%.
  • 在sdAb-DB数据集上表现出极具竞争力的结果.
  • 展示了框架在基于序列的亲和力预测中的有效性.

结论:

  • DLP-Affinity为基于序列的抗体亲和力预测提供了一个强大而准确的解决方案.
  • 双级深度学习方法有效地捕获了本地和全球蛋白质特征.
  • 该框架对涉及多种抗体格式和有限结构数据的应用具有前景.