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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

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

3.8K
3.8K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.5K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.5K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Protein and Protein Structure02:15

Protein and Protein Structure

79.5K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.5K

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相关实验视频

Updated: Jul 1, 2025

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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CollaPPI:一种协作学习框架,用于预测蛋白质与蛋白质之间的相互作用.

Wenjian Ma, Xiangpeng Bi, Huasen Jiang

    IEEE journal of biomedical and health informatics
    |March 11, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了CollaPPI,这是一种用于预测蛋白质与蛋白质相互作用 (PPI) 的新型深度学习框架. 通过在相互作用的蛋白质和相关的生物领域之间实现知识共享,CollaPPI提高了准确性.

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    相关实验视频

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    A Protocol for Computer-Based Protein Structure and Function Prediction
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    Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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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
    • 机器学习 机器学习

    背景情况:

    • 蛋白与蛋白相互作用 (PPI) 对于理解生物机制至关重要.
    • 实验性PPI的确定是昂贵和耗时的.
    • 现有的深度学习方法经常独立处理蛋白质,缺少协作知识.

    研究的目的:

    • 为PPI预测开发一种高效,准确的深度学习方法.
    • 解决目前方法中忽视知识共享的局限性.
    • 通过增强PPI预测,提高对生物过程的理解.

    主要方法:

    • 提出了一个名为CollaPPI的协作学习框架.
    • 整合了蛋白质层面的协作,以便在蛋白质对之间分享知识.
    • 在生物领域 (蛋白质功能,亚细胞位置) 进行知识互补的综合任务级合作.

    主要成果:

    • 在两个PPI基准上,CollaPPI表现优于最先进的方法.
    • 在额外的PPI类型预测任务中取得了出色的概括能力.
    • 有效地利用蛋白质和相关生物数据之间的共享知识.

    结论:

    • 在数据驱动的PPI预测中,CollaPPI提供了显著的进步.
    • 协作式学习方法提高了准确性和概括性.
    • 该框架提供了对蛋白相互作用及其生物相关性的更全面的理解.