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Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs

Nature Biotechnology volume 25, pages 651–655 (2007)Cite this article

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.

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Figure 1: The proteomics workflow modeled and definitions of success rate and relative dynamic range, RDR.
Figure 2: Simulations of proteome analysis of Homo sapiens.
Figure 3: Simulation results revealing the influence of the order by which various design parameters are varied on success rate and RDR30.

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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.

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Author notes

  1. Jan Eriksson and David Fenyö: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry, Swedish University of Agricultural Sciences, Box 7015, SE-750 07, Uppsala, Sweden

    Jan Eriksson

  2. The Rockefeller University, 1230 York Avenue, New York, 10021, New York, USA

    David Fenyö

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  1. Jan Eriksson
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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)

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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

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