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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

13.7K
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...
13.7K
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
Ligand Binding Sites02:40

Ligand Binding Sites

13.5K
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...
13.5K
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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.9K
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:
13.9K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

3.4K
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相关实验视频

Updated: Sep 18, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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PLAIG:使用一种新的基于相互作用的图形神经网络框架来预测蛋白质 - 连接物结合的亲和力.

Madhav V Samudrala1, Somanath Dandibhotla2, Arjun Kaneriya3

  • 1College of Arts and Sciences, The University of Virginia, Charlottesville, Virginia 22903, United States.

ACS bio & med chem Au
|June 25, 2025
PubMed
概括

我们开发了PLAIG,一个图形神经网络 (GNN) 模型,用于准确地预测蛋白质-连接体结合亲和力. 通过整合分子拓和相互作用,PLAIG提高了概括性,超过了药物发现的现有方法.

关键词:
结合性亲和力是一种结合性亲和力.这是一个新的预测预测.发现药物的发现.组合学习组合学习图表神经网络的神经网络蛋白质 - 连接物 相互作用 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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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.
  • 机器学习是机器学习.
  • 药物发现 药物发现

背景情况:

  • 准确预测蛋白质 - 配体结合亲和力对于有效的药物发现至关重要.
  • 现有的机器学习模型由于不完整的特征表示而难以进行概括.
  • 在开发强大的预测模型方面,过度装配仍然是一个挑战.

研究的目的:

  • 开发一种通用机器学习框架,用于预测蛋白质 - 配体结合亲缘关系.
  • 解决现有模型在概括和过拟合方面的局限性.
  • 创建一种新的方法,整合结构和交互特征,以提高预测准确度.

主要方法:

  • 开发了PLAIG,一个图形神经网络 (GNN) 框架,以图形形式表示绑定复合体.
  • 集成的蛋白质-连接体相互作用和分子拓学,以捕捉独特的特征.
  • 采用主要组件分析 (PCA) 和集体学习 (堆积回归器) 来减轻过度拟合.

主要成果:

  • PLAIG在PDBbind v.2019精炼集上实现了0.78的皮尔森相关系数 (PCC),在v.2016核心集上达到0.82.
  • 对DUDE-Z的外部验证显示平均AUC为0.69,区分了活性干与诱.
  • 将PLAIG与其他方法集成的混合模型达到0.88的平均PCC,最高为0.98.

结论:

  • PLAIG提供了一种强大的和通用的方法来预测蛋白质-联体结合亲和力.
  • 基于GNN的框架有效地整合了各种分子特征,优于现有的模型.
  • 未来的工作重点将集中在结合对接方法和评估de novo配体的性能上.