Truly comprehensive proteome analysis is highly desirable in systems biology and biomarker discovery efforts. But complete proteome characterization has been hindered by the dynamic range and detection sensitivity of experimental designs, which are not adequate to the very wide range of protein abundances. Experimental designs for comprehensive analytical efforts involve separation followed by mass spectrometry–based identification of digested proteins. Because results are generally reported as a collection of identifications with no information on the fraction of the proteome that was missed, they are difficult to evaluate and potentially misleading. Here we address this problem by taking a holistic view of the experimental design and using computer simulations to estimate the success rate for any given experiment. Our approach demonstrates that simple changes in typical experimental designs can enhance the success rate of proteome analysis by five- to tenfold.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).
Anderson, N.L. & Anderson, N.G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).
Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
Wang, H. et al. Intact-protein-based high-resolution three-dimensional quantitative analysis system for proteome profiling of biological fluids. Mol. Cell. Proteomics 4, 618–625 (2005).
Ishihama, Y. Proteomic LC-MS systems using nanoscale liquid chromatography with tandem mass spectrometry. J. Chromatogr. A. 1067, 73–83 (2005).
Cargile, B.J., Bundy, J.L., Freeman, T.W. & Stephenson, J.L., Jr. Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification. J. Proteome Res. 3, 112–119 (2004).
Coon, J.J., Syka, J.E., Shabanowitz, J. & Hunt, D.F. Tandem mass spectrometry for peptide and protein sequence analysis. Biotechniques 38, 519–523 (2005).
Fenyo, D. Identifying the proteome: software tools. Curr. Opin. Biotechnol. 11, 391–395 (2000).
Johnson, R.S., Davis, M.T., Taylor, J.A. & Patterson, S.D. Informatics for protein identification by mass spectrometry. Methods 35, 223–236 (2005).
Eriksson, J., Chait, B.T. & Fenyo, D. A statistical basis for testing the significance of mass spectrometric protein identification results. Anal. Chem. 72, 999–1005 (2000).
Eriksson, J. & Fenyo, D. Probity: a protein identification algorithm with accurate assignment of the statistical significance of the results. J. Proteome Res. 3, 32–36 (2004).
Krokhin, O.V. et al. An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS. Mol. Cell. Proteomics 3, 908–919 (2004).
Acknowledgements
We thank R.C. Beavis, B.T. Chait, I. Cristea, S. Gohil, T. Jovanovic, J.C. Padovan and B.M. Ueberheide for helpful advice, discussions and assistance with the experiments.
Author information
Authors and Affiliations
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Fig. 1
Simulation results for human tissue showing that lowering the detection limit is equivalent to increasing the amount loaded. (PDF 114 kb)
Supplementary Fig. 2
Simulation results for human tissue showing that improving the MS dynamic range is equivalent to improving the peptide separation. (PDF 114 kb)
Supplementary Fig. 3
Simulations of proteome analysis of H. sapiens displaying the effect of changing the amount loaded on the RPC column and the effect of peptide separation for an experimental design with 30000 proteins. (PDF 115 kb)
Supplementary Fig. 4
Simulations of proteome analysis of H. sapiens displaying the effect of improving protein separation and the effect of changing the amount loaded on the RPC column and the effect of peptide separation for an experimental design with 300 proteins. (PDF 125 kb)
Supplementary Fig. 5
The effect on success rate of changing the total survival probability. (PDF 92 kb)
Supplementary Fig. 6
Simulation results for human tissue showing the effect of changing the shape (semi-Gaussian) of the protein amount distribution. (PDF 71 kb)
Supplementary Fig. 7
Simulation results for human tissue showing the effect of changing the shape (constant) of the protein amount distribution. (PDF 66 kb)
Supplementary Fig. 8
Simulation results for human tissue showing the effect of changing the width (σ=1) of the protein amount distribution. (PDF 29 kb)
Supplementary Fig. 9
Simulations of proteome analysis of H. sapiens using a retention time model for RPC separation. (PDF 112 kb)
Supplementary Fig. 10
Simulation results revealing the influence of the order by which various design parameters are varied using a retention time model for RPC separation. (PDF 103 kb)
Supplementary Fig. 11
The effect on success rate of changing the level of random variation of MS signal intensities for peptides. (PDF 110 kb)
Supplementary Fig. 12
The effect of limited MS-sampling. (PDF 125 kb)
Supplementary Fig. 13
The effect of repeat analysis. (PDF 55 kb)
Supplementary Fig. 14
Experimental observations of the effect of improved separation for on-line MS analysis. (PDF 98 kb)
Rights and permissions
About this article
Cite this article
Eriksson, J., Fenyö, D. Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs. Nat Biotechnol 25, 651–655 (2007). https://doi.org/10.1038/nbt1315
Issue Date:
DOI: https://doi.org/10.1038/nbt1315
This article is cited by
-
Multiple reaction monitoring assay based on conventional liquid chromatography and electrospray ionization for simultaneous monitoring of multiple cerebrospinal fluid biomarker candidates for Alzheimer’s disease
Archives of Pharmacal Research (2016)
-
On protein abundance distributions in complex mixtures
Proteome Science (2013)
-
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
Nature Biotechnology (2008)