Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.
T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States.
J Proteome Res. 2024 Jul 5;23(7):2332-2342. doi: 10.1021/acs.jproteome.3c00887. Epub 2024 May 24.
Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.
在这里,我们介绍了 FLiPPR,即 FragPipe LiP(有限蛋白水解)处理器,这是一种工具,可在 FragPipe 中进行初步搜索和定量后,方便对有限蛋白水解质谱(LiP-MS)实验的数据进行分析。LiP-MS 已成为一种可提供蛋白质结构的全蛋白质组信息的方法,并已应用于一系列生物学和生物物理问题。尽管 LiP-MS 可以使用标准实验室试剂和质谱仪进行,但与典型的定量蛋白质组学工作流程相比,分析数据可能会比较缓慢并且具有独特的挑战。为了解决这个问题,我们利用 FragPipe,然后在 FLiPPR 中处理其输出。FLiPPR 采用了一种特定的数据插补启发式方法,该方法在 LiP-MS 实验中仔细利用缺失数据,以报告最显著的结构变化。此外,FLiPPR 引入了一种数据合并方案和一种以蛋白质为中心的多重假设校正方案,使经过处理的 LiP-MS 数据集更健壮,冗余更少。当使用 FragPipe/FLiPPR 工作流程重新分析以前发表的数据时,这些改进可以增强统计趋势。我们希望 FLiPPR 将降低更多用户采用 LiP-MS 的门槛,标准化 LiP-MS 数据分析的统计程序,并系统化输出,以促进 LiP-MS 数据的更大规模整合。