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Sequential abundant ion fragmentation analysis (SAIFA): an alternative approach for phosphopeptide identification using an ion trap mass spectrometer. Anal Biochem 2011 Nov 15;418(2):197-203

Date

08/23/2011

Pubmed ID

21855524

Pubmed Central ID

PMC3188319

DOI

10.1016/j.ab.2011.07.026

Scopus ID

2-s2.0-80052778411 (requires institutional sign-in at Scopus site)   4 Citations

Abstract

Phosphorylation has been the most studied of all the posttranslational modifications of proteins. Mass spectrometry has emerged as a powerful tool for phosphomapping on proteins/peptides. Collision-induced dissociation (CID) of phosphopeptides leads to the loss of phosphoric or metaphosphoric acid as a neutral molecule, giving an intense neutral loss product ion in the mass spectrum. Dissociation of the neutral loss product ion identifies peptide sequence. This method of data-dependent constant neutral loss (DDNL) scanning analysis has been commonly used for mapping phosphopeptides. However, preferential losses of groups other than phosphate are frequently observed during CID of phosphopeptides. Ions that result from such losses are not identified during DDNL analysis due to predetermined scanning for phosphate loss. In this study, we describe an alternative approach for improved identification of phosphopeptides by sequential abundant ion fragmentation analysis (SAIFA). In this approach, there is no predetermined neutral loss molecule, thereby undergoing sequential fragmentation of abundant peak, irrespective of the moiety lost during CID. In addition to improved phosphomapping, the method increases the sequence coverage of the proteins identified, thereby increasing the confidence of protein identification. To the best of our knowledge, this is the first report to use SAIFA for phosphopeptide identification.

Author List

Chesnik M, Halligan B, Olivier M, Mirza SP



MESH terms used to index this publication - Major topics in bold

Amino Acid Sequence
Caseins
Ions
Mass Spectrometry
Phosphopeptides
Protein Processing, Post-Translational
Proteomics