Decision tree–driven tandem mass spectrometry for shotgun proteomics

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Mass spectrometry has become a key technology for modern large-scale protein sequencing. Tandem mass spectrometry, the process of peptide ion dissociation followed by mass-to-charge ratio (m/z) analysis, is the critical component for peptide identification. Recent advances in mass spectrometry now permit two discrete, and complementary, types of peptide ion fragmentation: collision-activated dissociation (CAD) and electron transfer dissociation (ETD) on a single instrument. To exploit this complementarity and increase sequencing success rates, we designed and embedded a data-dependent decision tree algorithm (DT) to make unsupervised, real-time decisions of which fragmentation method to use based on precursor charge and m/z. Applying the DT to large-scale proteome analyses of Saccharomyces cerevisiae and human embryonic stem cells, we identified 53,055 peptides in total, which was greater than by using CAD (38,293) or ETD (39,507) alone. In addition, the DT method also identified 7,422 phosphopeptides, compared to either 2,801 (CAD) or 5,874 (ETD) phosphopeptides.

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Figure 1: Comprehensive probability of a high-confidence peptide identification.
Figure 2: Overlap between various combinations of duplicate analyses of yeast peptides.
Figure 3: Schematic representation of the probabilistic decision tree.
Figure 4: Distribution of identified peptides using CAD, ETD or DT methods.
Figure 5: Overlap between various combinations of duplicate analyses of hESC phosphopeptides.


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We thank J. Griep-Raming, S. Horning, O. Lange, A. Makarov, J. Schwartz, M. Senko, G. Stafford and J. Syka (all at Thermo Scientific) for providing advice and technical assistance during the development of the ETD-Orbitrap instrumentation. We also thank B. Craig, S. Herbst, H. Coon and K. Thompson for growing and collecting the yeast, and J. Antosiewicz-Bourget and J. Thomson for growing the hESCs. We acknowledge J. Marto and S. Ficarro for advice with the Waters chromatography system. We thank G. Barrett-Wilt, D. Good, J. Keith, D. Phanstiel and A. Huhmer for helpful discussions. The University of Wisconsin, the Beckman Foundation, Eli Lilly and the US National Institutes of Health (NIH) (1R01GM080148 to J.J.C.) provided financial support for this work. G.C.M. and D.L.S. acknowledge support from the NIH predoctoral fellowships (Biotechnology Training Program, NIH 5T32GM08349 to G.C.M.; the Genomic Sciences Training Program, NIH 5T32HG002706 to D.L.S.).

Author information

G.C.M., D.L.S. and J.J.C. designed research; G.C.M. and D.L.S. performed research; G.C.M. modified the instrument; and G.C.M., D.L.S. and J.J.C. wrote the paper.

Note: Supplementary information is available on the Nature Methods website.

Correspondence to Joshua J Coon.

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Competing interests

J.J.C. is a co-inventor on two US patent applications related, in part, to the material presented here.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1, Supplementary Methods (PDF 287 kb)

Supplementary Data

List of unique peptide identifications (XLS 7892 kb)

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Swaney, D., McAlister, G. & Coon, J. Decision tree–driven tandem mass spectrometry for shotgun proteomics. Nat Methods 5, 959–964 (2008) doi:10.1038/nmeth.1260

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