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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
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,...
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相关实验视频

Updated: Jun 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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基于表面的多模式蛋白质 - 连接物结合亲缘关系预测.

Shiyu Xu1, Lian Shen2, Menglong Zhang2

  • 1National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Bioinformatics (Oxford, England)
|June 21, 2024
PubMed
概括
此摘要是机器生成的。

通过新的多式特征提取 (MFE) 框架,预测蛋白质-连接体结合亲和力得到了改进. 这种方法整合了蛋白质表面,3D结构和序列数据,以加强药物发现.

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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
  • 药物发现 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确预测蛋白质 - 联体结合亲和力对于药物发现和优化至关重要.
  • 目前的方法通常依赖于序列或结构数据,对蛋白质表面信息的探索有限,这对相互作用至关重要.
  • 由于简单的特征连接,现有的多式联运方法可能无法有效利用互补信息.

研究的目的:

  • 引入一种新的多式特征提取 (MFE) 框架,以改进蛋白质 - 连接体结合亲缘关系的预测.
  • 整合各种蛋白质数据模式,包括表面,3D结构和序列信息.
  • 通过结合交叉注意力机制来解决传统的多式联运特征融合的局限性.

主要方法:

  • 开发了一个多模式特征提取 (MFE) 框架.
  • 嵌入的蛋白质表面,3D结构和序列数据.
  • 利用交叉注意力机制来实现跨模式的特征对齐.

主要成果:

  • 在预测蛋白质-连接体结合亲和力方面取得了最先进的性能.
  • 通过废除研究证明了通过蛋白质表面信息的结合的有效性.
  • 验证了多式联运特征对齐的必要性,以提高预测准确度.

结论:

  • 拟议的MFE框架显著推进了蛋白质-连接体结合亲缘关系的预测.
  • 整合多模式蛋白质数据,特别是表面信息,与交叉注意力,对于最佳性能至关重要.
  • 该框架为更有效的药物查和优化提供了一个有希望的方向.