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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...
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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...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.8K
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:
12.8K
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 Networks02:26

Protein Networks

3.9K
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,...
3.9K

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相关实验视频

Updated: Jun 10, 2025

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

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ERL-ProLiGraph:增强对蛋白质-连接体图结构数据的表示学习,用于结合亲和力预测.

Gloria Geine Paendong1, Soualihou Ngnamsie Njimbouom1, Candra Zonyfar1

  • 1Department of Computer Science and Electronics Engineering, Sun Moon University, Chungcheongnam-do, Korea.

Molecular informatics
|October 15, 2024
PubMed
概括

预测蛋白质 - 配体结合亲和力 (PLBA) 对药物发现至关重要. 一个新的深度学习模型,ERL-ProLiGraph,使用图形表示来提高PLBA预测准确度,与现有方法相比.

关键词:
结合性亲和力是一种结合性亲和力.生物信息学是一种生物信息学.发现药物的发现.蛋白质连接物相互作用

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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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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.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确的蛋白质 - 连接物结合亲和力 (PLBA) 预测通过识别潜在的候选药物来加速药物开发.
  • 目前用于PLBA的计算方法通常依赖于简化分子表示,限制准确性.
  • 开发更高效,更精确的PLBA预测模型是计算化学的一个重大挑战.

研究的目的:

  • 引入一种基于深度学习的新方法,即在蛋白质-连接物图结构化数据上进行增强的表示学习,用于绑定亲和力预测 (ERL-ProLiGraph).
  • 利用蛋白质和配体的图形表示来捕获复杂的结构信息,以提高PLBA预测.
  • 提高计算方法的准确性和效率,以预测药物开发中的蛋白质-连接体相互作用.

主要方法:

  • 使用图形表示,节点表示原子结构,边缘表示化学键和空间关系.
  • 开发了一个深度学习模型 (ERL-ProLiGraph) 来学习这些图形结构和绑定亲和关系之间的相关性.
  • 采用基于图形的表示来捕捉复杂的分子相互作用,这对于结合亲和力预测至关重要.

主要成果:

  • 与之前的PLBA预测模型相比,ERL-ProLiGraph模型表现出更高的性能.
  • 基于图形的方法有效地捕获了基本的结构信息,从而提高了预测准确性.
  • 拟议的方法在预测蛋白质 - 配体结合亲缘关系方面表现出显著的有效性.

结论:

  • ERL-ProLiGraph代表了用于蛋白质-联体结合预测的计算技术的重大进展.
  • 该模型为PLBA预测提供了更准确,更有效的方法,有助于药物发现.
  • 基于图形的深度学习模型对计算药物设计的未来发展充满希望.