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Whole protein characterization and identification via gas-phase ion/ion and dissociation reactions in a quadrupole ion trap

By Jason Michael Hogan

Abstract

‘Top down’ protein analysis has been extended to ion trap mass spectrometry via the use of ion/ion proton transfer chemistry. Charge manipulation by ion/ion reactions is used in several ways to aid in protein ion analysis. Electrospray ionization of biomolecules produces multiple charge states that can complicate mixture analysis. Ion/ion proton transfer reactions can partially reduce the protein ion charge states, simplifying the analysis of complex mixtures. The use of “ion parking” allows protein ion signal from multiple charge states to be concentrated into a single protein charge state. After fragmentation of whole protein ions via collision-induced dissociation, ion/ion proton transfer reactions are used to reduce the charge states of the fragment ions to singly charged, simplifying spectral interpretation. Ion/ion proton transfer reactions also allow the formation of protein ion charge states not directly accessible by electrospray ionization. This can assist in the study of whole protein ion fragmentation as a function of precursor ion charge state. ‘Top down\u27’ protein analysis has been used in the identification of unknown proteins via database searching. Uninterpreted product ion mass spectra, obtained from whole protein ion fragmentation, were compared against the in silico fragmentation of the proteins contained in either a protein database or a translated genome database. A scoring algorithm has been developed in-house to aid in identifying unknown proteins by incorporating fragment ion abundances into the protein score for each candidate protein. Post-translational modifications in unknown proteins were identified, and potentially localized, by looking for mass shifts in the fragment ion mass values along with changes in the overall protein mass

Topics: Analytical chemistry|Biochemistry
Publisher: 'Purdue University (bepress)'
Year: 2005
OAI identifier: oai:docs.lib.purdue.edu:dissertations-5720
Provided by: Purdue E-Pubs
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