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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
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
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
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

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相关实验视频

Updated: Jul 15, 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

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结构感知图表注意力扩散网络用于蛋白质 - 连接物结合亲和力预测.

Mei Li, Ye Cao, Xiaoguang Liu

    IEEE transactions on neural networks and learning systems
    |September 26, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的结构意识图表注意力扩散网络 (SGADN),用于预测蛋白质-连接体结合亲缘关系. SGADN通过专注于债券和层次复杂结构来增强空间结构学习,优于现有的方法.

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

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    相关实验视频

    Last Updated: Jul 15, 2025

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    A Protocol for Computer-Based Protein Structure and Function Prediction
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    A Protocol for Computer-Based Protein Structure and Function Prediction

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    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:

    • 准确预测蛋白质 - 配体结合亲缘关系对于加速药物发现至关重要.
    • 现有的图形神经网络 (GNN) 方法模拟蛋白质-连接体复合体,但往往忽视了键和等级结构的重要性.
    • 这些局限性阻碍了准确的结合亲和力预测.

    研究的目的:

    • 开发一种新的图形神经网络 (GNN) 模型,即结构意识图形注意力扩散网络 (SGADN),以改进蛋白质 - 连接体结合亲缘关系的预测.
    • 将距离和角度信息结合起来,以便在蛋白质-连接体复合体内增强空间结构学习.
    • 通过强调债券的重要性和层次复杂结构来解决现有的GNN的局限性.

    主要方法:

    • 将蛋白质 - 连接体复合体建模为线图,其中键被表示为节点,并包含距离和角度信息.
    • 利用直线图注意力扩散层 (LGADLs) 来捕捉键结之间的远程相互作用,并完善空间结构学习.
    • 实施一个专注的聚合层 (APL) 以有效地学习和代表综合体内的等级结构.

    主要成果:

    • 与现有方法相比,拟议的SGADN模型在预测蛋白质 - 连接体结合亲缘关系方面表现优越.
    • 在两个基准数据集上进行了广泛的实验验证,证实了SGADN的有效性.
    • 该模型成功地结合了距离和角度信息,以有效地学习空间结构.

    结论:

    • 通过有效地学习空间结构和层次表示,SGADN在预测蛋白质-连接体结合亲缘关系方面取得了重大进展.
    • 该方法的重点是债券和远程相互作用,为分子复合体提供了更细致的理解.
    • SGADN对药物发现和开发中的应用非常有前途.