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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
Microtubule Associated Proteins (MAPs)01:42

Microtubule Associated Proteins (MAPs)

4.2K
Microtubule function and architecture are regulated by an array of specialized proteins called microtubule-associated proteins or MAPs. These proteins are widespread across different organisms and have conserved protein motifs, like the multi-TOG domain for tubulin binding found in the CLASP family of MAPs. Some MAPs are lineage-specific based on their conserved domains. Their functions depend upon the cytoskeletal architecture and cell type they are located within. In-plant cells, a specific...
4.2K

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相关实验视频

Updated: Jun 8, 2025

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
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Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

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使用Mapper和图形卷积网络预测蛋白质相互作用网络中的蛋白质复合体.

Leonardo Daou1, Eileen Marie Hanna1

  • 1Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon.

Computational and structural biotechnology journal
|November 4, 2024
PubMed
概括

MComplex使用动态基因表达和蛋白质相互作用来预测蛋白质复合体. 这种新的方法在识别蛋白质复合体方面优于现有的方法,推动了疾病研究.

科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白质复合体对于细胞功能和疾病机制至关重要.
  • 高通量实验产生了大量的蛋白质与蛋白质相互作用数据集.
  • 现有的计算方法通常依赖于静态蛋白相互作用网络.

研究的目的:

  • 开发一种先进的计算方法来预测蛋白质复合体.
  • 利用动态生物数据进行更准确的复杂识别.
  • 提高对细胞过程和疾病病理学的理解.

主要方法:

  • MComplex利用时间序列基因表达和蛋白质相互作用数据.
  • 一个时间网络是由一个生成对抗网络 (GAN) 与一个图形卷积网络 (GCN) 生成器生成和处理的.
  • 用修改的基于图形的Mapper算法进行嵌入分析,用于复杂的预测.

主要成果:

  • 与现有方法相比,MComplex表现出优越的性能.
  • 该方法在回忆和最大匹配比率方面取得了高分.
  • 综合得分证实了MComplex在综合评估措施中的有效性.
关键词:
生成性的对抗性网络.图表 卷积网络 卷积网络映射器算法 映射器算法蛋白质复合体是一种蛋白质复合体.蛋白质与蛋白质的相互作用拓学数据分析的分析.

更多相关视频

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

Published on: October 19, 2021

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

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相关实验视频

Last Updated: Jun 8, 2025

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

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

Published on: October 19, 2021

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

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结论:

  • MComplex为蛋白质复合体预测提供了一种强大而准确的方法.
  • 集成的动态数据增强了蛋白质复合体的识别.
  • 这种方法在疾病机制研究和治疗开发中具有潜在的应用.