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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.4K
Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K

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相关实验视频

Updated: May 28, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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深度交互意识:深度交互界面意识网络,用于从序列数据中改进抗原-抗体相互作用预测.

Yuhang Xia1, Zhiwei Wang1, Feng Huang1

  • 1College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 11, 2025
PubMed
概括
此摘要是机器生成的。

DeepInterAware是一个新的深度学习框架,使用序列数据准确预测抗原-抗体相互作用. 这种方法可以识别结合部位,并有助于用于治疗开发的抗体查.

关键词:
抗原抗体的相互作用.有约束力的自由能源变更.深度学习是一种深度学习.基于序列的预测.

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Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
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Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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相关实验视频

Last Updated: May 28, 2025

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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科学领域:

  • 生物化学 生物化学
  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学

背景情况:

  • 对抗原-抗体相互作用 (AAI) 的准确预测对于开发有效的人类疗法至关重要.
  • 对AAI的结构数据有限,在预测方面存在重大挑战.
  • 最近的进展表明,可以从序列数据中推断结构信息,从而实现基于序列的预测.

研究的目的:

  • 提出DeepInterAware,一个框架,将从序列数据中学到的交互接口信息用于AAI预测.
  • 评估DeepInterAware的性能与交互预测中的现有方法相比.
  • 探索DeepInterAware在识别结合点,检测突变和预测结合自由能量变化的能力.

主要方法:

  • 开发了DeepInterAware,这是一个深度学习框架,集成了序列衍生的交互接口信息和固有的序列特异性.
  • 在交互预测任务中应用DeepInterAware.
  • 利用HER2向抗体查实验来证明其实际应用.

主要成果:

  • 在交互预测方面,DeepInterAware的表现优于现有的方法.
  • 证明了有前途的诱导和转移能力,用于预测与未见的抗原/抗体的相互作用以及类似的任务.
  • 展示了识别潜在结合部位和检测抗原/抗体内的突变的能力.
  • 在HER2向抗体查实验中成功应用,识别结合抗体.

结论:

  • DeepInterAware是一种有效的工具,用于预测抗原-抗体相互作用,利用序列数据.
  • 该框架提供了独特的优势,包括对AAI和突变影响评估的机制性见解.
  • DeepInterAware显示了促进抗体查和治疗开发的巨大潜力.