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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.0K
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...
11.0K
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.6K
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...
2.6K
Proteomics01:33

Proteomics

7.5K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.5K
Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

7.4K
Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
Sec61 channel partners for cotranslational translocation
During cotranslational translocation, the Sec61 channel partners with the signal recognition particle (SRP), the signal recognition particle receptor (SR), and the ribosomes to transport the nascent polypeptide chain...
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Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

11.6K
Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
Protein sorting can be of two types: signal-based sorting and vesicle-based trafficking. In signal-based sorting, specific amino acid sequences called sorting signals target proteins to the proper location inside the cell either via gated transport or by protein translocation.  In gated transport, folded...
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相关实验视频

Updated: Jul 28, 2025

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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一个多式蛋白质表示框架,用于量化生物化学下游任务的可转移性.

Fan Hu1, Yishen Hu1, Weihong Zhang1

  • 1Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|May 30, 2023
PubMed
概括

这项研究介绍了MASSA,这是一个多模式的深度学习框架,用于蛋白质表示,集成序列,结构和功能. 马萨在各种生物任务中取得了最先进的结果,提高了对蛋白质的理解.

关键词:
下游任务下游任务多式联运是多式联运.蛋白质表示的蛋白质表示.可以转让的可转让性.

更多相关视频

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

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相关实验视频

Last Updated: Jul 28, 2025

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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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 蛋白质是生命的基础,有效的计算表示对于生物分析至关重要.
  • 目前的蛋白质表示方法通常依赖于基于文本的语言模型,忽视了蛋白质复杂的3D结构和功能.
  • 需要先进的蛋白质表征来捕获多模式生物数据.

研究的目的:

  • 开发一个多式深度学习框架 (MASSA),用于全面的蛋白质表示.
  • 为了整合蛋白质序列,结构和功能注释数据.
  • 评估框架在各种下游生物任务上的表现.

主要方法:

  • 提出了一个名为MASSA的多式联络深度学习框架.
  • 使用了多任务学习过程,并设定了五个预训练目标.
  • 包含大约100万个蛋白质序列,结构和功能注释.
  • 引入了一个基于最佳运输的指标来评估代表性的可转移性.

主要成果:

  • 在预测蛋白质特性 (稳定性,光),蛋白质-蛋白质相互作用和蛋白质-连接体相互作用方面取得了最先进的性能.
  • 在二次结构预测和远程同质检测方面表现出具有竞争力的结果.
  • 在功能空间分布和跨任务的适应性之间展示了强烈的相关性.

结论:

  • 马萨框架提供了一个强大的,细粒度的蛋白质域特征表示.
  • 多模式蛋白质表示显著提高了各种关键生物应用中的性能.
  • 最佳运输指标为学习过程和模型可转移性提供了有价值的见解.