Back to Search Start Over

TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.

Authors :
Basisty N
Shulman N
Wehrfritz C
Marsh AN
Shah S
Rose J
Ebert S
Miller M
Dai DF
Rabinovitch PS
Adams CM
MacCoss MJ
MacLean B
Schilling B
Source :
Journal of proteome research [J Proteome Res] 2023 Feb 03; Vol. 22 (2), pp. 311-322. Date of Electronic Publication: 2022 Sep 27.
Publication Year :
2023

Abstract

In spite of its central role in biology and disease, protein turnover is a largely understudied aspect of most proteomic studies due to the complexity of computational workflows that analyze in vivo turnover rates. To address this need, we developed a new computational tool, TurnoveR, to accurately calculate protein turnover rates from mass spectrometric analysis of metabolic labeling experiments in Skyline, a free and open-source proteomics software platform. TurnoveR is a straightforward graphical interface that enables seamless integration of protein turnover analysis into a traditional proteomics workflow in Skyline, allowing users to take advantage of the advanced and flexible data visualization and curation features built into the software. The computational pipeline of TurnoveR performs critical steps to determine protein turnover rates, including isotopologue demultiplexing, precursor-pool correction, statistical analysis, and generation of data reports and visualizations. This workflow is compatible with many mass spectrometric platforms and recapitulates turnover rates and differential changes in turnover rates between treatment groups calculated in previous studies. We expect that the addition of TurnoveR to the widely used Skyline proteomics software will facilitate wider utilization of protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.

Details

Language :
English
ISSN :
1535-3907
Volume :
22
Issue :
2
Database :
MEDLINE
Journal :
Journal of proteome research
Publication Type :
Academic Journal
Accession number :
36165806
Full Text :
https://doi.org/10.1021/acs.jproteome.2c00173