Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

13.2K
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...
13.2K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
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...
13.3K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

3.4K
3.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Rapamycin alleviates inflammation and muscle weakness, while altering the Treg/Th17 balance in a rat model of myasthenia gravis.

Bioscience reports·2017
Same author

Cross-resistance to purified Bt proteins, Bt corn and Bt cotton in a Cry2Ab2-corn resistant strain of Spodoptera frugiperda.

Pest management science·2017
Same author

Abnormal asymmetry in benign epilepsy with unilateral and bilateral centrotemporal spikes: A combined fMRI and DTI study.

Epilepsy research·2017
Same author

Lkb1 maintains T<sub>reg</sub> cell lineage identity.

Nature communications·2017
Same author

Corrigendum: EpCAM-dependent extracellular vesicles from intestinal epithelial cells maintain intestinal tract immune balance.

Nature communications·2017
Same author

Functional evidence that the self-renewal gene NANOG regulates esophageal squamous cancer development.

Biochemical and biophysical research communications·2017

相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.9K

用几何深度学习来预测与混合信息传递策略的蛋白质 - 连接体亲和力.

Jiaren Li, Huasen Jiang, Wenjian Ma

    IEEE journal of biomedical and health informatics
    |August 1, 2025
    PubMed
    概括

    这是一种新的几何深度学习方法HybridGeo,通过结合3D结构数据来增强蛋白质 - 连接体亲和力 (PLA) 预测. 这种方法实现了最先进的结果,提高了药物发现潜力.

    更多相关视频

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.0K
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    68.9K

    相关实验视频

    Last Updated: Sep 13, 2025

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
    10:21

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

    Published on: February 23, 2024

    2.9K
    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.0K
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    68.9K

    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:

    • 准确的蛋白质 - 配体亲和力 (PLA) 预测对于加速药物发现至关重要.
    • 当前的深度学习方法通常依赖于1D或2D表示,忽视关键的3D几何信息.
    • 假设3D空间特征在分子结合相互作用中起着重要作用.

    研究的目的:

    • 开发一种新的几何深度学习方法,HybridGeo,用于改进PLA预测.
    • 为了利用3D结构信息和混合信息传递策略来增强绑定亲和度建模.
    • 在不同的数据集上验证拟议模型的通用性和稳定性.

    主要方法:

    • 开发了HybridGeo,这是一种使用双视图学习的几何深度学习模型,用于分子内和分子间的原子相互作用.
    • 采用混合策略来聚合空间信息和几何图形变压器用于残留量级蛋白质口袋分析.
    • 在PDBbind数据集和三个外部测试集上训练和评估模型.

    主要成果:

    • 在PDBbind数据集上,HybridGeo以1.172的根平均平方误差 (RMSE) 实现了最先进的性能.
    • 该模型在三个外部测试集中表现出卓越的性能,表明强大的通用性和稳定性.
    • 废弃性研究证实了单个模块的有效性,案例研究强调了宏环化合物复合物的性能.

    结论:

    • HybridGeo有效地整合了3D几何特征,以准确地预测蛋白质-连接体亲和力.
    • 该模型的卓越性能和通用性为计算药物发现提供了有希望的进步.
    • 预测的生物解释性表明了指导合理药物设计的潜力.