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Systematic detection of functional proteoform groups from bottom-up proteomic datasets

To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating p...

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Autores principales: Bludau, Isabell, Frank, Max, Dörig, Christian, Cai, Yujia, Heusel, Moritz, Rosenberger, George, Picotti, Paola, Collins, Ben C., Röst, Hannes, Aebersold, Ruedi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217233/
https://www.ncbi.nlm.nih.gov/pubmed/34155216
http://dx.doi.org/10.1038/s41467-021-24030-x
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author Bludau, Isabell
Frank, Max
Dörig, Christian
Cai, Yujia
Heusel, Moritz
Rosenberger, George
Picotti, Paola
Collins, Ben C.
Röst, Hannes
Aebersold, Ruedi
author_facet Bludau, Isabell
Frank, Max
Dörig, Christian
Cai, Yujia
Heusel, Moritz
Rosenberger, George
Picotti, Paola
Collins, Ben C.
Röst, Hannes
Aebersold, Ruedi
author_sort Bludau, Isabell
collection PubMed
description To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.
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spelling pubmed-82172332021-07-09 Systematic detection of functional proteoform groups from bottom-up proteomic datasets Bludau, Isabell Frank, Max Dörig, Christian Cai, Yujia Heusel, Moritz Rosenberger, George Picotti, Paola Collins, Ben C. Röst, Hannes Aebersold, Ruedi Nat Commun Article To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets. Nature Publishing Group UK 2021-06-21 /pmc/articles/PMC8217233/ /pubmed/34155216 http://dx.doi.org/10.1038/s41467-021-24030-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bludau, Isabell
Frank, Max
Dörig, Christian
Cai, Yujia
Heusel, Moritz
Rosenberger, George
Picotti, Paola
Collins, Ben C.
Röst, Hannes
Aebersold, Ruedi
Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_full Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_fullStr Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_full_unstemmed Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_short Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_sort systematic detection of functional proteoform groups from bottom-up proteomic datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217233/
https://www.ncbi.nlm.nih.gov/pubmed/34155216
http://dx.doi.org/10.1038/s41467-021-24030-x
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