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相关概念视频

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.
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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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Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
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Updated: Jul 12, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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预测未经研究的暗酶的蛋白质和通路关联,使用模式受约束的知识图嵌入.

Mariah V Salcedo1, Nathan Gravel2, Abbas Keshavarzi3

  • 1Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America.

PeerJ
|October 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了RegPattern2Vec,这是一种新的生物信息学工具,使用知识图来预测研究不足的蛋白质激酶的功能. 该方法增强了对生物通路中的激酶作用的理解,有助于未来的研究.

关键词:
分类 分类 分类 分类.数据集成数据集成.药物发现 药物发现进化 进化 进化 进化 进化 进化 进化照亮可使用药物的基因组 (IDG)链接预测链接预测存在学 (Ontologies) 是一种存在学.路径预测路径预测随机步行 随机步行 随机步行信号网络是一个信号网络.

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科学领域:

  • 生物信息学和计算生物学
  • 分子生物学和基因组学
  • 系统生物学 系统生物学

背景情况:

  • 人类基因组编码了534种蛋白激酶,这是一个重要的药物标类别,包括许多研究不足的"暗"激酶.
  • 准确预测这些未被研究的激酶的功能是生物信息学中的一个关键挑战.
  • 现有的方法很难有效地利用复杂的生物数据进行功能预测.

研究的目的:

  • 开发一种新的图形挖掘方法,用于预测未经研究的激酶的蛋白质和通路关联.
  • 介绍 RegPattern2Vec,一个可扩展的图形嵌入方法,利用常规模式受约束的随机步行.
  • 通过整合各种生物数据来提高对激酶的功能预测的准确性和效率.

主要方法:

  • 采用图形挖掘方法,使用知识图 (KG) 来捕捉进化和功能背景.
  • 开发了RegPattern2Vec,一种使用常规模式受约束随机路径用于多种节点上下文采样的图形嵌入技术.
  • 整合了关于激酶,相互作用伙伴,翻译后修改,途径,细胞局部化和化学相互作用的数据,将其整合到以激酶为中心的KG中.

主要成果:

  • 与其他基于随机走路的图形嵌入方法相比,RegPattern2Vec表现出更好的准确性和效率.
  • 模型预测显示与来自实验验验证的蛋白质与蛋白质相互作用 (PPI) 数据的途径丰富数据重叠.
  • 产生了34个暗酶的高可靠性路径预测,并通过元路径分析说明了生物解释的案例研究.

结论:

  • RegPattern2Vec有效地采样多个节点类型,用于在生物知识图上进行链接预测.
  • 尚未研究的激酶,伪激酶和途径之间的预测关联为产生假设提供了基础.
  • 这种方法为探索尚未研究的激酶的功能格局和推动生物发现提供了有价值的工具.