multiFLEX-LF: a computational approach to quantify the modification stoichiometries in label-free proteomics data sets
- In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry ofprotein modifications is not readily available. To overcome this problem,we developed multiFLEX-LF, a computational tool that builds uponFLEXIQuant, which detects modified peptide precursors and quantifiestheir modification extent by monitoring the differences between observedand expected intensities of the unmodified precursors. multiFLEX-LFrelies on robust linear regression to calculate the modification extent of agiven precursor relative to a within-study reference. multiFLEX-LF cananalyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyzemodification dynamics and coregulated modifications, we hierarchicallyclustered the precursors of all proteins based on their computed relativemodification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C)In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry ofprotein modifications is not readily available. To overcome this problem,we developed multiFLEX-LF, a computational tool that builds uponFLEXIQuant, which detects modified peptide precursors and quantifiestheir modification extent by monitoring the differences between observedand expected intensities of the unmodified precursors. multiFLEX-LFrelies on robust linear regression to calculate the modification extent of agiven precursor relative to a within-study reference. multiFLEX-LF cananalyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyzemodification dynamics and coregulated modifications, we hierarchicallyclustered the precursors of all proteins based on their computed relativemodification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time pointsduring mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modificationevents. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and thequantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF candrive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies.multiFLEX-LF is available athttps://gitlab.com/SteenOmicsLab/multiflex-lf.…
Author details: | Pauline HiortORCiD, Christoph N. SchlaffnerORCiD, Judith A. Steen, Bernhard Y. RenardORCiDGND, Hanno SteenORCiD |
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DOI: | https://doi.org/10.1021/acs.jproteome.1c00669 |
ISSN: | 1535-3893 |
ISSN: | 1535-3907 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/35086334 |
Title of parent work (English): | Journal of proteome research |
Publisher: | American Chemical Society |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Date of first publication: | 2022/01/27 |
Publication year: | 2022 |
Release date: | 2024/04/11 |
Tag: | LC-MS; MS; PTM; bioinformatics tool; label-free quantification; modification stoichiometry; post-translational modification; quantification |
Volume: | 21 |
Issue: | 4 |
Number of pages: | 11 |
First page: | 899 |
Last Page: | 909 |
Funding institution: | U.S. National Institutes of Health [S10OD0107060, R01CA196703,; R01AI099204, U01AI124284, R01GM112007]; Deutsche Forschungsgemeinschaft; [RE3474/2-2]; HPI Research School of Data Science and Engineering |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften |
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie | |
Peer review: | Referiert |