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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

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:
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Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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...
12.7K
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
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-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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Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

135
Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
135

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相关实验视频

Updated: Jun 10, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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组合方法用于预测蛋白质 - 连接物结合亲和力.

Jiffriya Mohamed Abdul Cader1,2, M A Hakim Newton3,4, Julia Rahman5,6

  • 1School of Information and Communication Technology, Griffith University, Nathan Campus, Australia. jiffriya.cader@griffithuni.edu.au.

Scientific reports
|October 18, 2024
PubMed
概括

集结绑定亲和力 (EBA) 通过使用多种深度学习模型和输入功能,改善了蛋白质 - 连接体结合的预测. 这种方法提高了准确性和概括性,加速了药物发现.

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科学领域:

  • 计算化学和化学信息学
  • 人工智能在药物发现中的作用

背景情况:

  • 准确的蛋白质 - 配体结合亲和力预测对于计算机辅助药物发现至关重要.
  • 由于依赖单个模型,现有的深度学习方法往往缺乏准确性和概括性.

研究的目的:

  • 开发一种新的深度学习方法,即集束结合亲和力 (EBA),用于准确的蛋白质-连接体结合亲和力预测.
  • 通过组合多个深度学习模型,训练各种输入特征来提高预测准确性和概括性.

主要方法:

  • 训练了13个深度学习模型,使用5个输入特征的组合,结合交叉注意力和自我注意力层.
  • 探索了所有可能的模型组合,以确定用于结合亲和力预测的最佳组合.
  • 利用蛋白质 - 配体复合体的1D序列和结构特征,避免复杂的3D结构数据.

主要成果:

  • 在CASF2016基准数据集上获得了0.914的皮尔森相关系数 (R) 和0.957的根平均平方误差 (RMSE).
  • 与CAPLA预测器相比,CSAR-HiQ数据集的R值有超过15%的改善,RMSE有19%的改善.
  • 与最先进的方法相比,在五个基准数据集上的所有指标上表现出卓越的表现.

结论:

  • 该EBA方法显著提高了蛋白质 - 配体结合亲和力预测的准确性和稳定性.
  • 整体方法有效地利用各种模型和特征来增强预测能力.
  • 这项工作有助于通过提高潜在药物候选者的成功率来加速药物开发.