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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
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
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

7.9K
Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
7.9K
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

13.0K
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:
13.0K
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

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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一个多视角模型来预测蛋白质 - 配体结合的亲和力.

Xianfeng Zhang1, Yafei Li2, Jinlan Wang3

  • 1School of Computer and Electronic Information, Nanjing Normal University, Nanjing, 210023, China.

Interdisciplinary sciences, computational life sciences
|October 10, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种通用模型,用于解释多视角图表来预测蛋白质 - 配体结合的亲和力. 该方法通过抽象地表示蛋白质 - 连接体复合体来提高预测性能.

关键词:
结合性亲和力预测预测数据表示数据表示.图表神经网络的神经网络蛋白质语言模型的模型

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算生物学是一种计算生物学.
  • 化学信息学 化学信息学
  • 药物发现 药物发现

背景情况:

  • 准确预测蛋白质 - 连接物结合亲和力对于药物发现至关重要.
  • 传统方法通常会单独分析图形视角,导致解释性有限.
  • 蛋白质和连接体的异质性在结合亲和力预测中提出了挑战.

研究的目的:

  • 开发一个利用多视角图形信息的一般模型.
  • 为了提高蛋白质 - 配体结合亲和力预测的解释性和预测性性能.
  • 在计算建模中解决蛋白质和配体的异质性.

主要方法:

  • 使用多视角图表来表示蛋白质-连接体复合体.
  • 开发一个数据表示的一般抽象模型.
  • 结合策略来处理蛋白质和连接体的异质性.

主要成果:

  • 拟议的模型在蛋白质 - 配体结合亲和力预测方面取得了出色的预测性能.
  • 抽象表示策略提高了模型的可解释性.
  • 实验评估证实了公开数据集上的数据表示的有效性.

结论:

  • 开发的通用模型有效地利用了多视角图形信息,以改进蛋白质-连接物结合亲和力预测.
  • 该方法提供了更好的解释性和预测准确性,解决了该领域的关键挑战.
  • 这项研究展示了用于计算药物发现应用的有希望的数据表示策略.