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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
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.3K
VSEPR Theory for Determination of Electron Pair Geometries
34.3K
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 Organization01:24

Protein Organization

6.5K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.5K

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相关实验视频

Updated: Jul 5, 2025

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

68.7K

通过图形表示学习和集体学习改进基于结构的蛋白质-连接体亲和力预测.

Jia Guo1,2

  • 1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, P.R. China.

PloS one
|January 17, 2024
PubMed
概括

这项研究介绍了LGN,这是一个图形神经网络模型,通过提取独特的连接体特征来改善蛋白质-连接体结合亲和力预测. 这种方法提高了药物发现的准确性,与所有分子均处理的方法相比.

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习在生物化学中的应用

背景情况:

  • 预测蛋白质 - 配体结合亲和力对于加速药物发现至关重要.
  • 现有的图形神经网络方法可能会忽视蛋白质和连接体之间的数据异质性.
  • 来自配体的生物化学信息不完整可能会限制预测的准确性.

研究的目的:

  • 开发一个先进的图形神经网络模型,用于增强的蛋白质 - 连接体结合亲和力预测.
  • 通过结合特定的连接体特征提取来解决现有方法的局限性.
  • 改进蛋白质 - 连接体复合体内生物化学信息的探索.

主要方法:

  • 提出了LGN,一个基于图形神经网络的融合模型,包含额外连接体特征提取.
  • 使用交互指纹与基于连接体的特征一起.
  • 采用集体学习来增强对数据相似性的模型稳定性.

主要成果:

  • 在PDBbind 2016核心集上,LGN实现了0.842的皮尔森相关系数,单独超过了复杂图形特征 (0.807).
  • 与最先进的基线方法相比,证明了更高的性能.
  • 通过全面的实验验证了模型的合理化和概括.

更多相关视频

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

2.5K
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

1.9K

相关实验视频

Last Updated: Jul 5, 2025

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

68.7K
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

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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

Published on: January 26, 2024

1.9K

结论:

  • 拟议的LGN模型有效地捕捉了蛋白质-连接体复合体的本地和全球特征.
  • 整合连体特异性特征和相互作用指纹显著提高了结合亲缘关系预测的准确性.
  • 该模型显示出强大的概括性和稳定性,验证了其在现场的优越性.