Literature DB >> 21309581

More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS.

Annette Michalski1, Juergen Cox, Matthias Mann.   

Abstract

Shotgun proteomics entails the identification of as many peptides as possible from complex mixtures. Here we investigate how many peptides are detectable by high resolution MS in standard LC runs of cell lysate and how many of them are accessible to data-dependent MS/MS. Isotope clusters were determined by MaxQuant and stringently filtered for charge states and retention times typical of peptides. This resulted in more than 100,000 likely peptide features, of which only about 16% had been targeted for MS/MS. Three instrumental attributes determine the proportion of additional peptides that can be identified: sequencing speed, sensitivity, and precursor ion isolation. In our data, an MS/MS scan rate of 25/s would be necessary to target all peptide features, but this drops to less than 17/s for reasonably abundant peptides. Sensitivity is a greater challenge, with many peptide features requiring long MS/MS injection times (>250 ms). The greatest limitation, however, is the generally low proportion of the target peptide ion intensity in the MS/MS selection window (the "precursor ion fraction" or PIF). Median PIF is only 0.14, making the peptides difficult to identify by standard MS/MS methods. Our results aid in developing strategies to further increase coverage in shotgun proteomics.

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Year:  2011        PMID: 21309581     DOI: 10.1021/pr101060v

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


  193 in total

1.  Comprehensive and reproducible phosphopeptide enrichment using iron immobilized metal ion affinity chromatography (Fe-IMAC) columns.

Authors:  Benjamin Ruprecht; Heiner Koch; Guillaume Medard; Max Mundt; Bernhard Kuster; Simone Lemeer
Journal:  Mol Cell Proteomics       Date:  2014-11-13       Impact factor: 5.911

2.  The use of chromium(III) to supercharge peptides by protonation at low basicity sites.

Authors:  Changgeng Feng; Juliette J Commodore; Carolyn J Cassady
Journal:  J Am Soc Mass Spectrom       Date:  2014-11-14       Impact factor: 3.109

3.  Improved mass defect model for theoretical tryptic peptides.

Authors:  Indranil Mitra; Alexey V Nefedov; Allan R Brasier; Rovshan G Sadygov
Journal:  Anal Chem       Date:  2012-03-07       Impact factor: 6.986

4.  Occurrence of C-terminal residue exclusion in peptide fragmentation by ESI and MALDI tandem mass spectrometry.

Authors:  Mathieu Dupré; Sonia Cantel; Jean Martinez; Christine Enjalbal
Journal:  J Am Soc Mass Spectrom       Date:  2011-11-18       Impact factor: 3.109

5.  Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Authors:  Ludovic C Gillet; Pedro Navarro; Stephen Tate; Hannes Röst; Nathalie Selevsek; Lukas Reiter; Ron Bonner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-01-18       Impact factor: 5.911

6.  Analysis of protein mixtures from whole-cell extracts by single-run nanoLC-MS/MS using ultralong gradients.

Authors:  Thomas Köcher; Peter Pichler; Remco Swart; Karl Mechtler
Journal:  Nat Protoc       Date:  2012-04-12       Impact factor: 13.491

7.  Unbiased selective isolation of protein N-terminal peptides from complex proteome samples using phospho tagging (PTAG) and TiO(2)-based depletion.

Authors:  Geert P M Mommen; Bas van de Waterbeemd; Hugo D Meiring; Gideon Kersten; Albert J R Heck; Ad P J M de Jong
Journal:  Mol Cell Proteomics       Date:  2012-06-22       Impact factor: 5.911

8.  MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics.

Authors:  Dennis Goldfarb; Wei Wang; Michael B Major
Journal:  Bioinformatics       Date:  2015-12-17       Impact factor: 6.937

9.  mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

Authors:  Guoshou Teo; Sinae Kim; Chih-Chiang Tsou; Ben Collins; Anne-Claude Gingras; Alexey I Nesvizhskii; Hyungwon Choi
Journal:  J Proteomics       Date:  2015-09-15       Impact factor: 4.044

10.  Determining the Mitochondrial Methyl Proteome in Saccharomyces cerevisiae using Heavy Methyl SILAC.

Authors:  Katelyn E Caslavka Zempel; Ajay A Vashisht; William D Barshop; James A Wohlschlegel; Steven G Clarke
Journal:  J Proteome Res       Date:  2016-10-18       Impact factor: 4.466

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