Literature DB >> 35220718

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Deanna L Plubell1, Lukas Käll2, Bobbie-Jo Webb-Robertson3, Lisa M Bramer3, Ashley Ives4, Neil L Kelleher4, Lloyd M Smith5, Thomas J Montine6, Christine C Wu1, Michael J MacCoss1.   

Abstract

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Entities:  

Keywords:  post-translational modifications; protein grouping; proteoforms; quantitative analysis; quantitative proteomics

Mesh:

Substances:

Year:  2022        PMID: 35220718      PMCID: PMC8976764          DOI: 10.1021/acs.jproteome.1c00894

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  42 in total

1.  A statistical model for identifying proteins by tandem mass spectrometry.

Authors:  Alexey I Nesvizhskii; Andrew Keller; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2003-09-01       Impact factor: 6.986

2.  Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

Authors:  Bobbie-Jo M Webb-Robertson; Melissa M Matzke; Susmita Datta; Samuel H Payne; Jiyun Kang; Lisa M Bramer; Carrie D Nicora; Anil K Shukla; Thomas O Metz; Karin D Rodland; Richard D Smith; Mark F Tardiff; Jason E McDermott; Joel G Pounds; Katrina M Waters
Journal:  Mol Cell Proteomics       Date:  2014-12       Impact factor: 5.911

3.  Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

Authors:  Matthew The; Lukas Käll
Journal:  Mol Cell Proteomics       Date:  2018-11-27       Impact factor: 5.911

4.  Correlation between protein and mRNA abundance in yeast.

Authors:  S P Gygi; Y Rochon; B R Franza; R Aebersold
Journal:  Mol Cell Biol       Date:  1999-03       Impact factor: 4.272

5.  Kinetics of plasma apolipoprotein E isoforms by LC-MS/MS: a pilot study.

Authors:  Valentin Blanchard; Stéphane Ramin-Mangata; Stéphanie Billon-Crossouard; Audrey Aguesse; Manon Durand; Kevin Chemello; Brice Nativel; Laurent Flet; Maud Chétiveaux; David Jacobi; Jean-Marie Bard; Khadija Ouguerram; Gilles Lambert; Michel Krempf; Mikaël Croyal
Journal:  J Lipid Res       Date:  2018-03-14       Impact factor: 5.922

Review 6.  Tau: From research to clinical development.

Authors:  David M Holtzman; Maria C Carrillo; James A Hendrix; Lisa J Bain; Ana M Catafau; Laura M Gault; Michel Goedert; Eckhard Mandelkow; Eva-Maria Mandelkow; David S Miller; Susanne Ostrowitzki; Manuela Polydoro; Sean Smith; Marion Wittmann; Michael Hutton
Journal:  Alzheimers Dement       Date:  2016-05-04       Impact factor: 21.566

7.  Multibatch TMT Reveals False Positives, Batch Effects and Missing Values.

Authors:  Alejandro Brenes; Jens Hukelmann; Dalila Bensaddek; Angus I Lamond
Journal:  Mol Cell Proteomics       Date:  2019-07-22       Impact factor: 5.911

8.  How many human proteoforms are there?

Authors:  Ruedi Aebersold; Jeffrey N Agar; I Jonathan Amster; Mark S Baker; Carolyn R Bertozzi; Emily S Boja; Catherine E Costello; Benjamin F Cravatt; Catherine Fenselau; Benjamin A Garcia; Ying Ge; Jeremy Gunawardena; Ronald C Hendrickson; Paul J Hergenrother; Christian G Huber; Alexander R Ivanov; Ole N Jensen; Michael C Jewett; Neil L Kelleher; Laura L Kiessling; Nevan J Krogan; Martin R Larsen; Joseph A Loo; Rachel R Ogorzalek Loo; Emma Lundberg; Michael J MacCoss; Parag Mallick; Vamsi K Mootha; Milan Mrksich; Tom W Muir; Steven M Patrie; James J Pesavento; Sharon J Pitteri; Henry Rodriguez; Alan Saghatelian; Wendy Sandoval; Hartmut Schlüter; Salvatore Sechi; Sarah A Slavoff; Lloyd M Smith; Michael P Snyder; Paul M Thomas; Mathias Uhlén; Jennifer E Van Eyk; Marc Vidal; David R Walt; Forest M White; Evan R Williams; Therese Wohlschlager; Vicki H Wysocki; Nathan A Yates; Nicolas L Young; Bing Zhang
Journal:  Nat Chem Biol       Date:  2018-02-14       Impact factor: 15.040

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

Authors:  Isabell Bludau; Max Frank; Christian Dörig; Yujia Cai; Moritz Heusel; George Rosenberger; Paola Picotti; Ben C Collins; Hannes Röst; Ruedi Aebersold
Journal:  Nat Commun       Date:  2021-06-21       Impact factor: 14.919

10.  Cerebrospinal fluid proteomics implicates the granin family in Parkinson's disease.

Authors:  Melissa S Rotunno; Monica Lane; Wenfei Zhang; Pavlina Wolf; Petra Oliva; Catherine Viel; Anne-Marie Wills; Roy N Alcalay; Clemens R Scherzer; Lamya S Shihabuddin; Kate Zhang; S Pablo Sardi
Journal:  Sci Rep       Date:  2020-02-12       Impact factor: 4.379

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  1 in total

1.  Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

Authors:  Karin Schork; Michael Turewicz; Julian Uszkoreit; Jörg Rahnenführer; Martin Eisenacher
Journal:  PLoS One       Date:  2022-10-21       Impact factor: 3.752

  1 in total

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