Literature DB >> 19586016

Effect of dynamic exclusion duration on spectral count based quantitative proteomics.

Ying Zhang1, Zhihui Wen, Michael P Washburn, Laurence Florens.   

Abstract

To increase proteome coverage, dynamic exclusion (DE) is a widely used tool. When DE is enabled, more proteins can be identified, although the total spectral counts will decrease. To investigate the effects of DE duration on spectral-counting based quantitative proteomics, we analyzed the same sample via multidimensional protein identification technology while enabling different DE durations (15, 60, 90, 300, 600 s) or turning DE off. Normalized spectral abundance factors (NSAFs) measured for abundant proteins varied little with or without DE, while enabling DE lead to higher peptide counts, higher NSAFs, and better reproducibility of detection for proteins of relatively lower abundance. The optimal DE duration, which generated the maximum number of peptides, proteins, and peptides per protein, was observed to be 90 s in our settings. We developed a mathematical model for analyzing the effects of DE duration on peptide spectral counts. We found that the optimal DE duration depends on the average chromatographic peak width at the base of eluting peptides and mass spectrometry parameters, leading us to calculate an optimized DE duration of 97.9 s, in excellent agreement with our observations. In this study, we provide a systematic approach for the optimization of spectral counts for improved quantitative proteomics analysis.

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Year:  2009        PMID: 19586016     DOI: 10.1021/ac9004887

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  60 in total

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Journal:  Mol Biol Rep       Date:  2016-01-11       Impact factor: 2.316

Review 3.  The spectra count label-free quantitation in cancer proteomics.

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4.  Analysis of flooding-responsive proteins localized in the nucleus of soybean root tips.

Authors:  Setsuko Komatsu; Susumu Hiraga; Mohammad Zaman Nouri
Journal:  Mol Biol Rep       Date:  2014-01-03       Impact factor: 2.316

5.  Optimization of data-dependent acquisition parameters for coupling high-speed separations with LC-MS/MS for protein identifications.

Authors:  Darryl Johnson; Barry Boyes; Taylor Fields; Rachel Kopkin; Ron Orlando
Journal:  J Biomol Tech       Date:  2013-07

Review 6.  Liquid chromatography-mass spectrometry-based quantitative proteomics.

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Journal:  J Biol Chem       Date:  2011-06-01       Impact factor: 5.157

7.  A bayesian mixture model for comparative spectral count data in shotgun proteomics.

Authors:  James G Booth; Kirsten E Eilertson; Paul Dominic B Olinares; Haiyuan Yu
Journal:  Mol Cell Proteomics       Date:  2011-05-20       Impact factor: 5.911

8.  Quantitative proteomics demonstrates that the RNA polymerase II subunits Rpb4 and Rpb7 dissociate during transcriptional elongation.

Authors:  Amber L Mosley; Gerald O Hunter; Mihaela E Sardiu; Michaela Smolle; Jerry L Workman; Laurence Florens; Michael P Washburn
Journal:  Mol Cell Proteomics       Date:  2013-02-15       Impact factor: 5.911

9.  Comparative label-free LC-MS/MS analysis of colorectal adenocarcinoma and metastatic cells treated with 5-fluorouracil.

Authors:  Kerry M Bauer; Paul A Lambert; Amanda B Hummon
Journal:  Proteomics       Date:  2012-06       Impact factor: 3.984

Review 10.  Quantitative analysis of global phosphorylation changes with high-resolution tandem mass spectrometry and stable isotopic labeling.

Authors:  Hye Kyong Kweon; Philip C Andrews
Journal:  Methods       Date:  2013-04-21       Impact factor: 3.608

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