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相关概念视频

Proteomics01:33

Proteomics

7.1K
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...
7.1K

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相关实验视频

Updated: May 21, 2025

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
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Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

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全窗口数据独立获取方法,用于更深层的自上而下的蛋白质组学.

Chen Sun1, Wenjing Zhang1, Mowei Zhou2

  • 1School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.

Analytical chemistry
|March 22, 2025
PubMed
概括
此摘要是机器生成的。

一种新的机器学习辅助全窗口DIA (FW-DIA) 方法通过利用所有蛋白质离子进行改进的分析来增强自上而下的蛋白质组学 (TDP). 这种方法提高了信号强度和蛋白质序列覆盖率,用于全面的蛋白质形状表征.

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Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

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Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry
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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 分析化学 分析化学
  • 生物化学 生物化学

背景情况:

  • 顶向下蛋白质组学 (TDP) 对于描述蛋白质形式至关重要.
  • 在TDP中,自上而下的质谱 (TDMS) 面临分析挑战.
  • 尽管在其他领域取得了成功,但在TDP中数据独立采集 (DIA) 尚未得到充分利用.

研究的目的:

  • 为TDP开发新的DIA战略.
  • 引入一个机器学习辅助的全窗口DIA (FW-DIA) 方法.
  • 为了克服TDMS中前体离子分离的局限性.

主要方法:

  • 开发了一种机器学习辅助的全窗口DIA (FW-DIA) 方法.
  • 消除了对前体离子隔离的需要.
  • 利用所有前体蛋白离子进行双重质谱.

主要成果:

  • 与各种商用质谱仪实现了兼容性.
  • 信号强度大约提高了50倍.
  • 在模块化蛋白质分析中,蛋白质序列覆盖率提高了3倍.
  • 成功分析了五种蛋白质混合物和大肠杆菌的核糖体蛋白质.

结论:

  • FW-DIA是TDP的强大工具,提高了数据质量和覆盖范围.
  • 该方法提供了一种兼容且高效的方法,用于蛋白质形状的表征.
  • 机器学习集成推进了蛋白质组学中的DIA策略.