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相关概念视频

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
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
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
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
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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相关实验视频

Updated: Jul 11, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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多外ECIF:改进了扩展连接互动功能,用于准确的结合亲和力预测.

Koji Shiota1, Tatsuya Akutsu1

  • 1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan.

Bioinformatics advances
|November 6, 2023
PubMed
概括

我们改进了扩展连接性交互特征 (ECIF) 以通过纳入原子间距离来预测蛋白质-联体结合亲和力. 多层ECIF显著提高了预测准确性,超过了以前的方法.

科学领域:

  • 计算化学是一种计算化学.
  • 结构生物学是结构生物学.
  • 药物发现 药物发现

背景情况:

  • 扩展连接性交互特征 (ECIF) 是一种预测蛋白质-连接体结合亲和力的方法.
  • ECIF提供了详细的原子表示,并在2016年评分函数比较评估 (CASF-2016) 中表现良好.
  • ECIF的一个主要局限性是它无法充分考虑原子间距离.

研究的目的:

  • 为了研究有效的距离表示蛋白质-连接体 (P-L) 结合亲和力预测.
  • 开发改进的ECIF算法,包括原子间距离信息.

主要方法:

  • 开发了两个算法来增强ECIF的特征提取:多ECIF和加权ECIF.
  • 多层的ECIF将原子间距离划分为多个层.
  • 权重的ECIF将重视基于原子间距离的相互作用.

主要成果:

  • 与加权ECIF和原始ECIF相比,多ECIF表现优越.
  • 多式ECIF在CASF-2016评分功率中实现了0.877的皮尔森相关系数.
  • 该研究强调了距离表示在P-L结合亲和力预测中的重要性.

结论:

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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  • 纳入原子间距离显著提高了ECIF对蛋白质-连接体结合亲缘关系的预测能力.
  • 多式ECIF是准确的评分功能开发的一个有希望的进步.
  • 开发的方法和代码可供公众进一步研究.