Abstract

3DLC protein- and peptide-fractionation technique combined with iTRAQ-peptide labeling and Orbitrap mass spectrometry was employed to quantitate Paracoccus dentirificans total proteome with maximal coverage. This resulted in identification of 24,948 peptides representing 2627 proteins (FDR<0.01) in P. dentirificans wild type and ferB mutant strains grown in the presence or absence of methyl viologen as an oxidative stressor. The data were generated for assessment of FerB protein role in oxidative stress as published by Pernikářová et al.; proteomic responses to a methyl viologen-induced oxidative stress in the wild type and FerB mutant strains of P. denitrificans, J. Proteomics 2015;125:68–75. Dataset is supplied in the article.

Highlights

  • Proteome-wide dataset generated by iTRAQ-3DLCMS/MS technique for studying the role of FerB protein in oxidative stress in Paracoccus denitrificans

  • The highest coverage of Paracoccus denitrificans proteome available [1], more than four times improved in comparison with previous analyses [2,3]

  • Physiological significance analyzed and discussed in detail by Pernikářová et al [1]. iTRAQ-3DLC–MS/MS technique [4] modified here is well applicable for in-depth analysis of bacterial proteomes

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Summary

Data accessibility Supplementary data of the article

The highest coverage of Paracoccus denitrificans proteome available [1], more than four times improved in comparison with previous analyses [2,3]. 2627 proteins (FDR o0.01) were identified, representing 52.3% coverage of the putative proteome predicted from genome sequence. 34.3% coverage of membrane proteins with 1 to 25 transmembrane domains, GRAVY index of identified proteins ranged from À 1.56 to 1.21. Physiological significance analyzed and discussed in detail by Pernikářová et al [1]. ITRAQ-3DLC–MS/MS technique [4] modified here is well applicable for in-depth analysis of bacterial proteomes Physiological significance analyzed and discussed in detail by Pernikářová et al [1]. iTRAQ-3DLC–MS/MS technique [4] modified here is well applicable for in-depth analysis of bacterial proteomes

Bacteria growth and culture conditions
Sample preparation
Trypsin digestion and iTRAQ labeling
Fractionation of iTRAQ labeled peptides by HILIC
SCX removal of unreacted iTRAQ labels
Proteomics data analysis
Statistical analysis of proteomics data

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