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

Bludau, I., Frank, M., Doerig, C., Cai, Y., Heusel, M., Rosenberger, G., et al. (2021). Systematic detection of functional proteoform groups from bottom-up proteomic datasets. Nature Communications, 12(1): 3810. doi:10.1038/s41467-021-24030-x.

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 Creators:
Bludau, Isabell1, Author              
Frank, Max2, Author
Doerig, Christian2, Author
Cai, Yujia2, Author
Heusel, Moritz2, Author
Rosenberger, George2, Author
Picotti, Paola2, Author
Collins, Ben C.2, Author
Rost, Hannes2, Author
Aebersold, Ruedi2, Author
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2external, ou_persistent22              

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Free keywords: DATA-INDEPENDENT ACQUISITION; MASS-SPECTROMETRY; PROTEIN; NUCLEOPORIN; QUANTIFICATION; INFERENCE; TMEM106B; DRAFTScience & Technology - Other Topics;
 Abstract: 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. Many proteins exist in various proteoforms but detecting these variants by bottom-up proteomics remains difficult. Here, the authors present a computational approach based on peptide correlation analysis to identify and characterize proteoforms from bottom-up proteomics data.

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: 18
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 12 (1) Sequence Number: 3810 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723