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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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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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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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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.
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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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多任务生物试验预培训对蛋白质 - 配体结合亲缘关系的预测.

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  • 1Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, JinZhai Road, 230026, Anhui, China.

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这项研究引入了多任务生物测试预训 (MBP),这是预测蛋白质 - 配体结合亲和力 (PLBA) 的新框架. MBP利用具有多种亲和度标签的大型数据集来改善药物发现中的模型概括性.

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生物测试生物测试图表神经网络的神经网络预先培训的培训前培训蛋白联体结合亲和关系 蛋白联体结合亲和关系

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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
  • 机器学习 机器学习

背景情况:

  • 预测蛋白质 - 配体结合亲和力 (PLBA) 对药物发现至关重要.
  • 目前用于PLBA预测的深度学习模型受到数据稀缺和概括问题的限制.
  • 像ChEMBL这样现有的大规模亲和数据集具有不一致的标签和实验条件.

研究的目的:

  • 为基于结构的PLBA预测提出一个新的预培训框架,即多任务生物测试预培训 (MBP).
  • 为了构建一个全面的预训练数据集,ChEMBL-Dock,包含超过300k的亲和度标签和2.8M的对接3D结构.
  • 通过从多样化和杂的数据中学习强大的结构知识,提高PLBA预测模型的概括能力.

主要方法:

  • 开发了多任务生物测试预培训 (MBP) 框架.
  • 创建了ChEMBL-Dock数据集,整合了各种亲和标签和3D结构.
  • 采用多任务学习来预测生物试验中的不同亲和标签和相对排名.

主要成果:

  • MBP在基于结构的PLBA预测方面表现出显著的能力.
  • 预培训方法有效地学习可转移的结构知识.
  • 开发的ChEMBL-Dock数据集解决了现有的亲和数据的局限性.

结论:

  • MBP是PLBA预测的第一个亲和力预训模型.
  • 该框架显示了推动药物发现的巨大潜力.
  • MBP为处理杂和多样化的生物试验数据提供了强大的解决方案.