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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.0K
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:
13.0K
GPCR Desensitization01:12

GPCR Desensitization

6.1K
G protein-coupled receptor (GPCR) signaling plays a crucial role in cell functioning. GPCR desensitization is an equally essential process. It allows cells to respond to changing environments and regain sensitivity to new stimuli while preventing unnecessary stimulation when no longer needed. Prolonged exposure to stimuli leads to GPCR desensitization. It involves blocking the receptors from binding and activating additional G proteins. This inhibits activation of downstream effectors, thereby...
6.1K

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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

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TrGPCR:基于动态深度转移学习的GPCR-利干结合亲和力预测.

Yaoyao Lu, Runhua Zhang, Tengsheng Jiang

    IEEE journal of biomedical and health informatics
    |August 23, 2023
    PubMed
    概括

    这项研究引入了TrGPCR,这是一种深度转移学习方法,用于预测G蛋白结合受体 (GPCR) 连接体结合亲和力. 它通过使用二级结构克服了数据的局限性,并提高了对现有模型的预测准确性.

    科学领域:

    • 计算化学是一种计算化学.
    • 生物信息学是一种生物信息学.
    • 药物发现 药物发现

    背景情况:

    • 预测G蛋白结合受体 (GPCR) - 配体结合亲和力对于药物开发至关重要,但面临挑战.
    • 目前的数据驱动方法通常需要3D蛋白质结构,这些结构通常是不可用的.
    • 现有的方法可能忽略了关键的二次结构信息,并且对深度学习的数据不足.

    研究的目的:

    • 开发一个深度转移学习模型,TrGPCR,以准确预测GPCR-连接物结合亲缘关系.
    • 为了应对有限的GPCR数据的挑战,用于训练机器学习模型.
    • 为了将蛋白质二次结构作为预测特征.

    主要方法:

    • 实施了使用动态转移学习的深度转移学习方法.
    • 使用绑定数据库 (BindingDB) 作为源域和GLASS数据库作为目标域.
    • 介绍了蛋白质二次结构 (口袋) 作为亲和力预测的特征.

    主要成果:

    • 与DeepDTA模型相比,TrGPCR模型显示出更好的预测准确性.
    • 在根平均平方误差 (RMSE) 中取得了5.2%的改善.
    • 在平均平方误差 (MAE) 中实现了4.5%的改善.

    更多相关视频

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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    Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay
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    Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay

    Published on: March 10, 2020

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    相关实验视频

    Last Updated: Jul 18, 2025

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
    10:21

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

    Published on: February 23, 2024

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    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

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    Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay
    09:03

    Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay

    Published on: March 10, 2020

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    结论:

    • 通过转移学习,TrGPCR有效地解决了GPCR数据不足的问题.
    • 蛋白质二次结构的包含增强了结合亲和力预测.
    • 这种方法为药物发现中的GPCR - 连接物结合亲缘关系预测提供了更有效,更准确的方法.