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相关概念视频

Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.2K
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...
13.2K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.6K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.3K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.3K
Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.3K

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相关实验视频

Updated: Sep 10, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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PF-AGCN:一种基于蛋白质相互作用的自适应图形卷积网络

Shumin Yang1, Yuhan Su1, Yuchen Lin1

  • 1School of Electronic Science and Engineering, Xiamen University, 361005, Fujian, China.

Bioinformatics (Oxford, England)
|August 26, 2025
PubMed
概括

预测蛋白质与蛋白质相互作用对于理解生物过程至关重要. 我们的新 PF-AGCN 方法通过将层次功能和蛋白质相互作用图表与先进的深度学习技术集成, 准确地捕获复杂的 PPI.

关键词:
图形卷积网络蛋白质功能预测蛋白质语言模型

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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相关实验视频

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科学领域:

  • 生物信息学
  • 计算生物学
  • 系统生物学

背景情况:

  • 蛋白与蛋白的相互作用是生物过程的基础.
  • 准确的PPI预测对于了解蛋白质功能至关重要.
  • 现有的方法难以捕捉PPI的复杂,分层性质.

研究的目的:

  • 开发一个先进的深度学习框架来准确预测蛋白质相互作用.
  • 克服捕获分层和复杂PPI的现有方法的局限性.

主要方法:

  • 拟议的PF-AGCN是一个自适应图形卷积网络.
  • 使用了两个图形结构:一个函数图 (基因本体学术语) 和一个蛋白质图 (直接相互作用).
  • 整合了蛋白质语言模型与堆叠扩展的因果卷积神经网络进行序列和结构分析.

主要成果:

  • 在广泛的实验中,PF-AGCN表现出卓越的预测准确性.
  • 这种方法有效地保存了原始的生物结构,同时学习了新的关系.
  • 实现了全球序列语义和本地结构模式的协同融合.

结论:

  • PF-AGCN在蛋白质相互作用预测方面提供了显著的进步.
  • 该框架能够整合多样化的生物数据,从而提高预测性能.
  • 开发的模型为生物研究提供了强大的工具.