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Computational prediction of proteotypic peptides for quantitative proteomics

Nature Biotechnology volume 25, pages 125–131 (2007)Cite this article

Abstract

Mass spectrometry–based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

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Figure 1: Proteomic data sets allow the identification of preferentially observed (proteotypic) peptides.
Figure 2: Peptide description by a numerical matrix of physicochemical properties identifies properties that discriminate between proteotypic and unobserved peptides.
Figure 3: Evaluation of the prediction ability of proteotypic peptide predictors and application of the yeast PAGE-ESI predictor to human proteins.

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Acknowledgements

The authors are grateful to Julien Gagneur for fruitful discussions and the Cellzome biochemistry, mass spectrometry and informatics teams for generating and managing data. The work was supported in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under contract N01-HV-28179.

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Authors and Affiliations

  1. Institute for Systems Biology, 1441 N. 34th Street, Seattle, 98103, Washington, USA

    Parag Mallick, Mark R Flory, Hookeun Lee, Daniel Martin, Jeffrey Ranish, Brian Raught & Ruedi Aebersold

  2. Cedars-Sinai Medical Center, 8750 W. Beverly Blvd, Los Angeles, 90048, California, USA

    Parag Mallick

  3. University of California, Los Angeles, 607 Charles E. Young Drive East, Box 951569, Los Angeles, 90095-1569, California, USA

    Parag Mallick & Sharon S Chen

  4. Cellzome AG, Meyerhofstrasse 1, Heidelberg, 69117, Germany

    Markus Schirle, Robert Schmitt, Thilo Werner & Bernhard Kuster

  5. Institute of Molecular Systems Biology, ETH Zurich and Faculty of Science, University of Zurich, Switzerland

    Hookeun Lee

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Contributions

P.M., data (yeast MUDPIT-ESI), data analysis, idea and concept, wrote most of manuscript. M.S., data (yeast PAGE-MALDI, human PAGE-ESI), data mining, wrote part of manuscript. S.C., data (yeast MUDPIT-ESI), idea and concept. M.F., data (yeast MUDPIT-ICAT). H.L, data (yeast MUDPIT-ICAT), D.M., data (yeast MUDPIT-ESI). J.R., data (yeast MUDPIT-ESI, MUDPIT-ICAT). B.R., data (yeast MUDPIT-ICAT). R.S., computation underlying Figure 1b. T.W., data (yeast PAGE-ESI). B.K., idea and concept, wrote part of manuscript. R.A., idea and concept, wrote part of manuscript.

Corresponding authors

Correspondence to Bernhard Kuster or Ruedi Aebersold.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Number of Peptide Observations/Protein in Model Mixture.

Supplementary Fig. 2

Histogram of number of confidently predicted proteotypic peptides/protein.

Supplementary Fig. 3

Intersection of Proteins and Peptides among Experiment Types.

Supplementary Fig. 4

Cysteine 2D Histogram.

Supplementary Fig. 5

Mass Distribution of Observed Peptides.

Supplementary Fig. 6

Length (Size) Distribution of Observed Peptides.

Supplementary Table 1

Description of 4 large scale datasets used in study.

Supplementary Table 2

List of observed proteins used in study.

Supplementary Table 3

List of experimentally derived proteotypic peptides used in study.

Supplementary Table 4

Interrogated Physico-Chemical Properties and their discrimination potential for PAGE_ESI .

Supplementary Table 5

Interrogated Physico-Chemical Properties and their discrimination potential for PAGE_MALDI.

Supplementary Table 6

Interrogated Physico-Chemical Properties and their discrimination potential for MUDPIT_ESI.

Supplementary Table 7

Interrogated Physico-Chemical Properties and their discrimination potential for MUDPIT_ICAT.

Supplementary Table 8

Comparison of empirically observed peptide presence with those made by prediction for human γ–secretase.

Supplementary Table 9

Predicted Proteotypic Peptides for Yeast.

Supplementary Table 10

Predicted Proteotypic Peptides for Human.

Supplementary Table 11

Extended Predicted Proteotypic Peptides for Yeast.

Supplementary Table 12

Extended Predicted Proteotypic Peptides for Human.

Supplementary Table 13

Intersection of Proteins by Experimental Approach.

Supplementary Table 14

Intersection of Proteotypic Peptides by Experimental Approach.

Supplementary Table 15

Intersection of Proteins with Predicted Proteotypic Peptides by Experimental Approach.

Supplementary Table 16

Intersection of High Confidence Predicted Proteotypic Peptides by Experimental Approach.

Supplementary Results

Supplementary Discussion

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Mallick, P., Schirle, M., Chen, S. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25, 125–131 (2007). https://doi.org/10.1038/nbt1275

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