Evaluation of Parameters for Confident Phosphorylation Site Localization Using an Orbitrap Fusion Tribrid Mass Spectrometer.
Samantha FerriesSimon PerkinsPhilip J BrownridgeAmy CampbellPatrick A EyersAndrew R JonesClaire E EyersPublished in: Journal of proteome research (2017)
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.
Keyphrases
- mass spectrometry
- high resolution
- ms ms
- liquid chromatography
- high performance liquid chromatography
- high throughput
- gas chromatography
- ultra high performance liquid chromatography
- electronic health record
- capillary electrophoresis
- high resolution mass spectrometry
- tandem mass spectrometry
- protein kinase
- big data
- single cell
- machine learning
- liquid chromatography tandem mass spectrometry
- multiple sclerosis
- magnetic resonance
- stem cells
- simultaneous determination
- binding protein
- deep learning
- bone marrow
- protein protein