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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
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 Sites02:40

Ligand Binding Sites

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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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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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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Protein Networks02:26

Protein Networks

4.5K
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,...
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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深度学习驱动的蛋白质-连接物结合亲和力预测:数据,架构,培训和评估.

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    此摘要是机器生成的。

    深度学习 (DL) 模型为药物发现提供了高效的蛋白质-连接体结合亲和力 (PLA) 预测. 本综述指导研究人员培训DL模型,解决数据,可解释性和生物相关性方面的挑战,以改善药物设计.

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    科学领域:

    • 计算生物学 计算生物学
    • 药物发现 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 (PLA) 对药物发现至关重要.
    • 深度学习 (DL) 模型为实验测试和传统评分函数提供了一个计算效率高的替代方案.
    • 知识差距阻碍了在PLA的DL模型设计中有效地整合生物学和计算洞察力.

    研究的目的:

    • 提供一个全面的指南,用于训练DL模型用于PLA预测.
    • 探索包括数据集,数据处理,模型架构,培训策略和评估在内的关键考虑因素.
    • 讨论DL在PLA中对药物发现的预测中的应用和挑战.

    主要方法:

    • 对DL进行PLA预测的当前文献的综述.
    • 在DL模型开发中的关键因素分析:数据,可解释性,生物可信性.
    • 探索培训策略和评估方法的探索.

    主要成果:

    • DL模型显示出快速和可扩展的PLA预测的前景.
    • 挑战包括数据异质性,模型可解释性和确保生物相关性.
    • 有效的DL模型培训需要仔细考虑多个相互关联的因素.

    结论:

    • 通过精确的PLA预测,DL提供了一种强大的方法来加速药物发现.
    • 弥合计算生物学和DL之间的差距对于最佳模型开发至关重要.
    • 需要进一步的研究来克服当前的挑战,并充分利用DL在这个领域.