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相关概念视频

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...
12.5K
Protein Networks02:26

Protein Networks

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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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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

1.8K
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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3.7K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Protein Complex Assembly02:41

Protein Complex Assembly

10.6K
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...
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相关实验视频

Updated: Jun 25, 2025

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Published on: March 3, 2015

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帕拉CPI:一个并行图卷积网络用于化合物-蛋白相互作用预测.

Longxin Zhang, Wenliang Zeng, Jingsheng Chen

    IEEE/ACM transactions on computational biology and bioinformatics
    |May 24, 2024
    PubMed
    概括

    一个新的并行图卷积网络,ParaCPI,增强了药物发现的化合物-蛋白相互作用 (CPI) 预测. 这种模型显著提高了识别未知的CPI的准确性,加速了新药的开发.

    科学领域:

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

    背景情况:

    • 准确的化合物-蛋白相互作用 (CPI) 识别对于有效的药物发现至关重要.
    • 现有的CPI预测模型在实际药物发现应用中面临局限性.
    • 利用丰富的生物知识来预测未知的CPI是一个活跃的研究领域.

    研究的目的:

    • 引入一个新的并行图卷积网络模型,ParaCPI,用于增强CPI预测.
    • 从已知的数据预测未知CPI的准确性和有效性.
    • 提供一种加速药物发现过程的工具.

    主要方法:

    • 开发了ParaCPI模型,一个平行图卷积网络.
    • 在ParaCPI模型中的化合物的独特特征表示结构.
    • 在五个公共数据集上进行实验验证,将ParaCPI与最先进的 (SOTA) 模型进行比较.

    主要成果:

    • 与SOTA模型相比,ParaCPI在三个冷启动设置 (26.75%,23.84%,14.68%) 中显示了曲线下面面积 (AUC) 的显著性能增长.
    • 案例研究实验证实了ParaCPI在预测未知的CPI方面的卓越能力.
    • 与现有的SOTA模型相比,ParaCPI表现出更高的准确性和更强的概括能力.

    更多相关视频

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    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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    相关实验视频

    Last Updated: Jun 25, 2025

    Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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    Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

    Published on: March 3, 2015

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    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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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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    结论:

    • 帕拉CPI模型提供了一种更有效的方法来预测化合物-蛋白质相互作用.
    • 与目前的方法相比,ParaCPI显示出了显著的改进,特别是在具有挑战性的冷启动场景中.
    • 这种模型有可能通过改善预测新型化合物-蛋白质相互作用来加速药物发现.