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一个半自动化工作流程,用于基于DIA的全球发现到路径驱动的PRM分析

Jennifer Guergues¹, Jessica Wohlfahrt¹, John M Koomen²

¹Department of Molecular Biosciences, University of South Florida, Tampa, Florida, USA.

|September 5, 2024

概括

一个新的Python脚本简化了使用数据独立获取 (DIA) 数据为目标蛋白质组学的并行反应监测 (PRM) 方法开发. 这种工具提高了蛋白质定量精度,并减少了复杂蛋白质组分析中缺失的数据.

科学领域:

蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.
分析化学分析化学

背景情况:

目标蛋白质组学,包括并行反应监测 (PRM),提供精确的蛋白质检测和定量.
数据独立获取 (DIA) 提供了深度蛋白质组覆盖,但可能有缺失的数据.
从DIA数据开发PRM方法是复杂且耗时的.

研究的目的:

开发一个Python脚本,从DIA数据中快速生成PRM方法.
在TIMS-TOF平台上简化和加快针对性蛋白质组学工作流.
提高定量精度,减少蛋白质组学中缺失的数据.

主要方法:

开发了一个Python脚本,使用DIA输出和目标列表生成PRM方法.
该脚本使用HeLa细胞溶解物的DIA数据进行了评估.
来自Ingenuity Pathway的路径信息引导PRM方法生成的指导性路径分析用于"调节亡"路径.

主要成果:

该脚本成功生成了一个路径驱动的PRM方法.
随后的PRM分析显示了更好的染色学数据和更高的定量精度.
达到百分之百的低于10%的变化系数 (CV),CV中位数为2.9%.

结论:

Python 脚本从 DIA 数据中显著简化和加速 PRM 方法的开发.
这种方法提高了针对性蛋白质组学的测量精度和数据完整性.
该脚本可以适应各种数据输出和仪器类型,提供多功能框架.

关键词:

在 PRM-PASEF 中.灭症 (apoptosis) 是一种死亡的过程.发现验证发现验证方法开发自动化自动化开放脚本开放脚本平行积累串行碎片化 (PASEF) 的方法平行反应监测 (PRM) 是指对反应进行平行监测.基于路径的分析.

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