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相关概念视频

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,...
4.5K
Protein Networks02:26

Protein Networks

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2.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.0K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.0K
Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

5.4K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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一个局部-全球多视图扩散变化图自编码器用于 lncRNA-蛋白相互作用预测.

Dongdong Mao, Ying Sun

    IEEE journal of biomedical and health informatics
    |October 31, 2025
    PubMed
    概括

    这项研究引入了一种新的计算模型,LG-MDVGA,用于预测长非编码RNA-蛋白相互作用 (LPIs). 该模型增强了特征融合,并捕捉了复杂的生物分子相互作用,以提高生物数据分析的准确性和概括性.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 基因组学就是基因组学.
    • 生物信息学是一种生物信息学.

    背景情况:

    • 长非编码RNAs (lncRNAs) 在细胞过程和疾病中起着至关重要的作用.
    • 预测lncRNA-蛋白相互作用 (LPIs) 是至关重要的,但受到有限的数据和不充分的特征工程的挑战.
    • 现有的计算方法,包括变量图形自动编码器 (VGAE),在与远程交互和数据概括方面扎.

    研究的目的:

    • 开发一个先进的计算框架,本地-全球多视图扩散变量图自动编码器 (LG-MDVGA),用于准确的LPI预测.
    • 克服现有方法在特征融合,数据利用和捕捉复杂的生物分子关系方面的局限性.
    • 提高LPI预测模型在多种生物数据集中的通用性和预测性能.

    主要方法:

    • 引入了LG-MDVGA,集成了一个特征构建和融合模块,参数化特征矩阵通过反向传播进行更新.
    • 为 lncRNA 和蛋白质采用适应性局部多模特征矩阵来捕获局部特征.
    • 通过自主监督学习进行集成的全球多空间协作计算,以利用有限的数据和捕获全球特征.
    • 开发了一个扩散变量图自编码器 (DVGA),以更好地模拟复杂的LPI模式和远程相互作用.

    主要成果:

    • 在预测LPIs方面,LG-MDVGA显著优于现有方法.

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  • 该模型在五个不同的数据集中表现出卓越的性能,包括对其他三种生物实体类型的预测.
  • 在预测生物实体之间的关联方面实现了高精度和增强的概括性.

    • 结论:

    • LG-MDVGA代表了计算LPI预测的重大进步.
    • 该模型能够整合本地和全球特征,并捕捉复杂的相互作用,导致卓越的准确性和通用性.
    • LG-MDVGA显示了发现新型LPIs和预测各种生物实体之间的关联的巨大潜力.