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相关概念视频

The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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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:
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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...
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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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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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相关实验视频

Updated: Jun 26, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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距离加上注意结合亲和力预测的注意力.

Julia Rahman1, M A Hakim Newton2,3, Mohammed Eunus Ali4

  • 1School of Information and Communication Technology, Griffith University, 170 Kessels Rd, Nathan, 4111, QLD, Australia. julia.rahman@griffithuni.edu.au.

Journal of cheminformatics
|May 12, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用原子距离和注意力机制的新方法,用于准确预测蛋白质-连接体结合亲和力,显著提高了药物开发效率. 通过捕捉特定的分子相互作用,DAAP模型提高了预测.

关键词:
注意力 注意力 注意力 注意力结合性亲缘关系是一种结合性亲缘关系.深度学习是一种深度学习.距离矩阵是一个距离矩阵.捐赠者-接受者关系水性 水性 水性

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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
  • 在生物信息学中的机器学习.

背景情况:

  • 蛋白质-配体结合亲和力对于药物开发至关重要,但准确的预测仍然具有挑战性.
  • 当前的深度学习方法通常使用资源密集型或分子相互作用的间接表示.
  • 有效捕获蛋白质-连接体相互作用是提高亲和力预测准确性的关键.

研究的目的:

  • 开发一种新的计算方法,精确地预测蛋白质 - 配体结合的亲和力.
  • 通过使用原子级特征和注意力机制,改进特定蛋白质-连接体相互作用的表现.
  • 通过增强的亲和力预测,减少与药物开发相关的时间和成本.

主要方法:

  • 提出了一种名为"距离加注意力对亲和预测 (DAAP) "的方法.
  • 利用了原子级距离特征,结合了捐赠者-接受者关系,疏水性和pi堆叠相互作用.
  • 采用注意力机制来捕捉不同级别的交互效应,并采用五种模型的整体方法.

主要成果:

  • 在CASF-2016数据集上,DAAP取得了最先进的表现,R=0.909,RMSE=0.987,MAE=0.745,SD=0.988,CI=0.876.
  • 在5个额外的基准数据集上显示了显著的性能改进 (2%至37%).
  • 该方法有效地捕捉了特定的蛋白质-连接体相互作用,优于现有的计算方法.

结论:

  • 通过利用距离特征和注意力机制,DAAP方法在预测蛋白质-连接体结合亲和力方面取得了重大进展.
  • 这种方法为药物发现和开发提供了更准确,更有效的工具.
  • 该模型捕获复杂的结合模式的能力提高了其在识别潜在药物候选者的实用性.