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Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer
Author(s) -
Samantha Ferries,
Simon Perkins,
Philip Brownridge,
A. Campbell,
Patrick A. Eyers,
Andrew R. Jones,
Claire E. Eyers
Publication year - 2017
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.7b00337
Subject(s) - orbitrap , phosphopeptide , phosphoproteomics , fragmentation (computing) , mass spectrometry , tandem mass spectrometry , chemistry , phosphorylation , computational biology , computer science , protein phosphorylation , biology , chromatography , biochemistry , protein kinase a , operating system
Confident identification of sites of protein phosphorylation by mass spectrometry (MS) is essential to advance understanding of phosphorylation-mediated signaling events. However, the development of novel instrumentation requires that methods for MS data acquisition and its interrogation be evaluated and optimized for high-throughput phosphoproteomics. Here we compare and contrast eight MS acquisition methods on the novel tribrid Orbitrap Fusion MS platform using both a synthetic phosphopeptide library and a complex phosphopeptide-enriched cell lysate. In addition to evaluating multiple fragmentation regimes (HCD, EThcD, and neutral-loss-triggered ET(ca/hc)D) and analyzers for MS/MS (orbitrap (OT) versus ion trap (IT)), we also compare two commonly used bioinformatics platforms, Andromeda with PTM-score, and MASCOT with ptmRS for confident phosphopeptide identification and, crucially, phosphosite localization. Our findings demonstrate that optimal phosphosite identification is achieved using HCD fragmentation and high-resolution orbitrap-based MS/MS analysis, employing MASCOT/ptmRS for data interrogation. Although EThcD is optimal for confident site localization for a given PSM, the increased duty cycle compared with HCD compromises the numbers of phosphosites identified. Finally, our data highlight that a charge-state-dependent fragmentation regime and a multiple algorithm search strategy are likely to be of benefit for confident large-scale phosphosite localization.

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