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Interpretation of Tandem Mass Spectra of Posttranslationally Modified Peptides

  • Jakob Bunkenborg
  • Rune Matthiesen
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2051)

Abstract

Tandem mass spectrometry provides a sensitive means of analyzing the amino acid sequence of peptides and modified peptides by providing accurate mass measurements of precursor and fragment ions. Modern mass spectrometry instrumentation is capable of rapidly generating many thousands of tandem mass spectra and protein database search engines have been developed to match the experimental data to peptide candidates. In most studies there is a schism between discarding perfectly valid data and including nonsensical peptide identifications—this is currently managed by establishing a false discovery rate (FDR) but for modified peptides it calls for an understanding of tandem mass spectrometry data. Manual evaluation of the data and perhaps experimental cross-checking of the MS data can save many months of experimental work trying to do biological follow-ups based on erroneous identifications. Especially for posttranslationally modified peptides there is a need for careful consideration of the data because search algorithms seldom have been optimized for the identification of modified peptides and because there are many pitfalls for the unwary. This chapter describes some of the issues that should be considered when interpreting and validating tandem mass spectra and gives some useful tables to aid in this process.

Key words

Proteomics Posttranslational modifications Mass spectrometry Database searching 

Notes

Acknowledgments

R.M. is supported by Fundação para a Ciência e a Tecnologia (FCT investigator program 2012). iNOVA4Health—UID/Multi/04462/2013, a program financially supported by Fundação para a Ciência e Tecnologia/Ministério da Educação e Ciência, through national funds and cofunded by FEDER under the PT2020 Partnership Agreement is acknowledged.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Jakob Bunkenborg
    • 1
  • Rune Matthiesen
    • 2
  1. 1.AlphalyseOdenseDenmark
  2. 2.Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências MédicasUniversidade NOVA de LisboaLisboaPortugal

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