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Identification of post-translational modifications by blind search of mass spectra

Nature Biotechnology volume 23, pages 1562–1567 (2005)Cite this article

Abstract

Most tandem mass spectrometry (MS/MS) database search algorithms perform a restrictive search that takes into account only a few types of post-translational modifications (PTMs) and ignores all others. We describe an unrestrictive PTM search algorithm, MS-Alignment, that searches for all types of PTMs at once in a blind mode, that is, without knowing which PTMs exist in nature. Blind PTM identification makes it possible to study the extent and frequency of different types of PTMs, still an open problem in proteomics. Application of this approach to lens proteins resulted in the largest set of PTMs reported in human crystallins so far. Our analysis of various MS/MS data sets implies that the biological phenomenon of modification is much more widespread than previously thought. We also argue that MS-Alignment reveals some uncharacterized modifications that warrant further experimental validation.

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Figure 1: PTM selection.
Figure 2: Spectral alignment.
Figure 3: Site verification.
Figure 4: Spectrum coverage.

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Acknowledgements

This project was supported by National Institutes of Health grant NIGMS 1-R01-RR16522. We are grateful to Brian Searle and Larry David for making their lens data set available and to Larry David, Katalin Medzihradszky and Philip Wilmarth for many useful discussions. Production of the lens data set was supported by National Eye Institute grant EY007755. This research was supported in part by the UCSD FWGrid Project, NSF Research Infrastructure Grant Number EIA-0303622. Production of the IKKb data set was supported by NIH grant R01GM65325 and by the Pew Scholars Program.

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Author notes

  1. Dekel Tsur and Stephen Tanner: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0404, California, USA

    Dekel Tsur, Vineet Bafna & Pavel A Pevzner

  2. Bioinformatics Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0419, California, USA

    Stephen Tanner

  3. Molecular Microbiology and Immunology, School of Medicine, Univ. Southern California, 2011 Zonal Avenue, HMR 401, Los Angeles, 90033, California, USA

    Ebrahim Zandi

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  1. Dekel Tsur
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  2. Stephen Tanner
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  4. Vineet Bafna
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  5. Pavel A Pevzner
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Corresponding author

Correspondence to Stephen Tanner.

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Supplementary information

Supplementary Fig. 1

Spectra from the ISB spectra were searched against a database containing valid proteins (37) and human nr (90,000). (PDF 32 kb)

Supplementary Fig. 2

Receiver Operating Characteristic (ROC) curve for the SVM score on the ISB data set. (PDF 117 kb)

Supplementary Table 1

Summary of validated modification sites over Lens proteins, compared with results reported by OpenSea (Searle et al, 2005). (PDF 7 kb)

Supplementary Table 2

PTM site count matrix for IKKb dataset (1,072 sites total). (PDF 11 kb)

Supplementary Table 3

Modifications on Lens data-set. (PDF 47 kb)

Supplementary Methods (PDF 92 kb)

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Tsur, D., Tanner, S., Zandi, E. et al. Identification of post-translational modifications by blind search of mass spectra. Nat Biotechnol 23, 1562–1567 (2005). https://doi.org/10.1038/nbt1168

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