Article | Published:

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics

Nature Methods volume 14, pages 513520 (2017) | Download Citation

Abstract

There is a need to better understand and handle the 'dark matter' of proteomics—the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry–based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein–RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

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Acknowledgements

We thank R. Beavis for helpful discussions, N. Bandeira and S. Na for help with MODa software, and E. Huttlin for assisting with the transfer of raw MS data from the AP–MS study. This work was funded in part by grants from the NIH (R01GM94231 and U24CA210967 to A.I.N.).

Author information

Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

    • Andy T Kong
    •  & Alexey I Nesvizhskii
  2. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.

    • Andy T Kong
    • , Felipe V Leprevost
    • , Dmitry M Avtonomov
    • , Dattatreya Mellacheruvu
    •  & Alexey I Nesvizhskii

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Contributions

A.T.K. and A.I.N. conceived the project. A.T.K. developed the algorithm, wrote the software, and analyzed the results. A.I.N. assisted with the algorithm development and software design, analyzed the results, and supervised the entire project. F.V.L., D.M.A., and D.M. contributed to software development and data analysis. A.T.K. and A.I.N. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alexey I Nesvizhskii.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10

  2. 2.

    Supplementary Protocol

    MSFragger Manual

Excel files

  1. 1.

    Supplementary Table 1

    Analysis times for a single file (b1906_293T_proteinID_01A_QE3_122212) in HEK293 dataset using different search engines.

  2. 2.

    Supplementary Table 2

    List of mass spectrometry data files analyzed from each dataset and their corresponding number of MS/MS spectra.

  3. 3.

    Supplementary Table 3

    List of top 500 detected features in mass shift histogram with potential explanations.

  4. 4.

    Supplementary Table 4

    Mass shift localization by data set.

  5. 5.

    Supplementary Table 5

    Number of peptide ions and PSMs identified in narrow-window and open searching by bait protein in AP-MS dataset.

  6. 6.

    Supplementary Table 6

    Peptide identifications in ETHE1 AP–MS experiments using narrow-window and mass-tolerant searches.

  7. 7.

    Supplementary Table 7

    List of genes associated with 'small molecule metabolic process' that have a large increase in identified bait peptide ions.

  8. 8.

    Supplementary Table 8

    List of identified peptides in RNA–protein cross-linking experiment.

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DOI

https://doi.org/10.1038/nmeth.4256

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