Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS

Author:

Deberneh Henock M.,Sadygov Rovshan G.ORCID

Abstract

Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D2O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.

Funder

NIGMS of the National Institutes of Health

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Numbers of Exchangeable Hydrogens from LC–MS Data of Heavy Water Metabolically Labeled Samples;Journal of the American Society for Mass Spectrometry;2024-07-26

2. Mass Spectrometric Proteomics 2.0;International Journal of Molecular Sciences;2024-03-04

3. Flexible Quality Control for Protein Turnover Rates Using d2ome;International Journal of Molecular Sciences;2023-10-25

4. A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling;Scientific Data;2023-09-19

5. Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling;Journal of Proteome Research;2023-01-24

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