FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics
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%).
KeywordsProteomics Peptide fractionation Mass spectrometry Bottom-up proteomics Liquid chromatography
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.
- 1.Ferguson PL, Smith RD. Proteome analysis by mass spectrometry. Annu Rev Biophys Biomol Struct. 2003;32:399–424. https://doi.org/10.1146/annurev.biophys.32.110601.141854.CrossRefGoogle Scholar
- 7.Nagaraj N, Alexander Kulak N, Cox J, Neuhauser N, Mayr K, Hoerning O, et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol Cell Proteomics. 2012;11:M111.013722. https://doi.org/10.1074/mcp.M111.013722.CrossRefGoogle Scholar
- 11.Aguilar M. Reversed-phase high-performance liquid chromatography. In: Aguilar MI, editors. HPLC of Peptides and Proteins. New Jersey: Humana Press; 2004. pp. 9–22. https://doi.org/10.1385/1-59259-742-4:9.
- 12.Mant CT, Hodges RS. Analysis of peptides by high-performance liquid chromatography. In: Eckenhoff RG, Dmochowski IJ, editors. Methods in enzymology. New York: Academic Press; 1996. pp. 3–50. https://doi.org/10.1016/S0076-6879(96)71003-0.
- 17.Dwivedi RC, Spicer V, Harder M, Antonovici M, Ens W, Standing KG, et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal Chem. 2008;80:7036–42. https://doi.org/10.1021/ac800984n.CrossRefGoogle Scholar
- 23.Choi M, Eren-Dogu ZF, Colangelo C, Cottrell J, Hoopmann MR, Kapp EA, et al. ABRF proteome informatics research group (iPRG) 2015 study: detection of differentially abundant proteins in label-free quantitative LC-MS/MS experiments. J Proteome Res. 2017;16:945–57. https://doi.org/10.1021/acs.jproteome.6b00881.CrossRefGoogle Scholar
- 25.Zhou Y, Gao J, Zhu H, Xu J, He H, Gu L, et al. Enhancing membrane protein identification using a simplified centrifugation and detergent-based membrane extraction approach. Anal Chem. 2018. https://doi.org/10.1021/acs.analchem.7b03710.
- 29.Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics. 2005;4:1265–72. https://doi.org/10.1074/mcp.M500061-MCP200.CrossRefGoogle Scholar