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相关概念视频

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
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-Protein Interfaces02:04

Protein-Protein Interfaces

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3.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.6K

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相关实验视频

Updated: May 16, 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

Published on: January 26, 2024

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预测蛋白质-蛋白质相互作用与可解释的双线性注意网络.

Yong Han1, Shao-Wu Zhang2, Ming-Hui Shi2

  • 1MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Henan Judicial Police Vocational College, Zhengzhou, 450046, China.

Computer methods and programs in biomedicine
|April 2, 2025
PubMed
概括

本研究介绍了PPI-BAN,这是一个用于通过整合序列和3D结构数据来预测蛋白质与蛋白质相互作用 (PPI) 和其类型的新框架. PPI-BAN有效地识别了关键的交互点,改进了现有的方法.

关键词:
双线性注意力网络是一个双线性注意力网络.蛋白质与蛋白质的相互作用关系图神经网络的神经网络

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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 结构生物学 结构生物学

背景情况:

  • 蛋白与蛋白相互作用 (PPI) 对生物过程和疾病至关重要.
  • 实验性识别PPI是昂贵的和耗时的.
  • 现有的计算方法往往忽略了蛋白质结构信息,或者无法学习联合表示.

研究的目的:

  • 开发一个新的端到端框架,PPI-BAN,用于预测PPI及其相互作用类型.
  • 整合蛋白质序列和3D结构信息,以提高预测准确度.
  • 通过识别显著的相互作用站点来提高PPI预测的解释性.

主要方法:

  • PPI-BAN使用1D卷积 (Conv1D) 来进行序列特征提取.
  • 几何意识关系图神经网络 (GearNet) 用于3D结构特征学习.
  • 一个深层的双线注意网络 (BAN) 学习了联合序列结构特征,然后将它们连接起来进行预测.

主要成果:

  • 与最先进的方法相比,PPI-BAN在预测PPI及其类型方面表现优越.
  • 该框架有效地整合了序列和结构信息,以便进行可靠的预测.

结论:

  • PPI-BAN提供了一种有效的计算方法,用于预测蛋白质-蛋白质相互作用及其类型.
  • 该方法可以通过分析注意力重量地图来识别关键交互点,提供对分子机制的洞察力.