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Analytical and Bioanalytical Chemistry

, Volume 410, Issue 16, pp 3827–3833 | Cite as

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics

  • Elizaveta M. Solovyeva
  • Anna A. Lobas
  • Arthur T. Kopylov
  • Irina Y. Ilina
  • Lev I. Levitsky
  • Sergei A. Moshkovskii
  • Mikhail V. Gorshkov
Research Paper

Abstract

Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The “real world” sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer. The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%).

Graphical abstract

The analysis workflow employing FractionOptimizer software.

Keywords

Proteomics Peptide fractionation Mass spectrometry Bottom-up proteomics Liquid chromatography 

Notes

Acknowledgments

This work was supported by the Russian Science Foundation, project no. 14-14-00971. The authors thank Dr. Irina A. Tarasova and Mark V. Ivanov for helpful discussions.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

216_2018_1054_MOESM1_ESM.pdf (1.5 mb)
ESM 1 (PDF 1577 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Elizaveta M. Solovyeva
    • 1
    • 2
  • Anna A. Lobas
    • 2
  • Arthur T. Kopylov
    • 3
  • Irina Y. Ilina
    • 3
  • Lev I. Levitsky
    • 2
  • Sergei A. Moshkovskii
    • 3
    • 4
  • Mikhail V. Gorshkov
    • 2
  1. 1.Moscow Institute of Physics and Technology (State University)DolgoprudnyRussia
  2. 2.V.L. Talrose Institute for Energy Problems of Chemical PhysicsRASMoscowRussia
  3. 3.Institute of Biomedical ChemistryMoscowRussia
  4. 4.Pirogov Russian National Research Medical UniversityMoscowRussia

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