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相关概念视频

Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
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-Protein Interfaces02:04

Protein-Protein Interfaces

3.7K
3.7K
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.6K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.6K
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 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

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相关实验视频

Updated: Jun 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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一个端到端的知识图 (Knowledge Graph) 融合图 (Fused Graph) 神经网络,用于准确的蛋白质-蛋白质相互作用预测.

Jie Yang, Yapeng Li, Guoyin Wang

    IEEE/ACM transactions on computational biology and bioinformatics
    |October 24, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的AI模型,即知识图融合图神经网络 (KGF-GNN),用于准确的蛋白质-蛋白质相互作用 (PPI) 预测. 通过整合各种生物数据,KGF-GNN模型增强了对细胞机制的理解,并有助于药物开发.

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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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    Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
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    Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

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    相关实验视频

    Last Updated: Jun 9, 2025

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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    Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
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    科学领域:

    • 生物信息学是一种生物信息学.
    • 计算生物学 计算生物学
    • 生命科学中的人工智能

    背景情况:

    • 蛋白与蛋白相互作用 (PPI) 是细胞功能,疾病病理学和药物发现的基础.
    • 现有的人工智能 (AI) 用于PPI预测的方法经常遭受碎片化数据处理和低于最佳的特征提取.
    • 非端到端学习框架的局限性阻碍了对复杂生物网络的全面分析.

    研究的目的:

    • 为准确和全面的蛋白质-蛋白质相互作用 (PPI) 预测开发一种新的端到端学习模型.
    • 通过统一框架整合各种生物数据来解决当前人工智能方法的局限性.
    • 通过优化特征提取和融合过程来提高PPI的预测准确性.

    主要方法:

    • 构建一个蛋白质关联网络 (PAN),集成蛋白质,药物,疾病,RNA和蛋白质结构.
    • 图形神经网络 (GNN) 的应用,从PAN和PPI网络中提取拓和语义特征.
    • 用多层感知子来进行端到端的特征融合和PPI预测.

    主要成果:

    • 建议的知识图融合图神经网络 (KGF-GNN) 模型在PPI预测中表现出高准确性.
    • 在现实世界PPI数据集上,KGF-GNN显著优于现有的最先进模型.
    • 端到端的学习框架确保了优化的特征提取和融合,以提高预测.

    结论:

    • 该KGF-GNN模型提供了一个更精确的方法来预测蛋白质-蛋白质相互作用.
    • 这一进步对生物研究,疾病机制的理解和治疗开发有深远的影响.
    • 该研究强调了生物信息学中综合人工智能方法在推动生命科学方面的潜力.