title: Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument.
abstract: The recently-introduced Orbitrap Fusion mass spectrometry permits various types of MS2 acquisition methods. To date, these different MS2 strategies and the optimal data interpretation approach for each have not been adequately evaluated. This study comprehensively investigated the four MS2 strategies: HCD-OT (higher-energy-collisional-dissociation with Orbitrap detection), HCD-IT (HCD with ion trap, IT), CID-IT (collision-induced-dissociation with IT) and CID-OT on Orbitrap Fusion. To achieve extensive comparison and identify the optimal data interpretation method for each technique, several search engines (SEQUEST and Mascot) and post-processing methods (score-based, PeptideProphet, and Percolator) were assessed for all techniques for the analysis of a human cell proteome. It was found that divergent conclusions could be made from the same dataset when different data interpretation approaches were used and therefore requiring a relatively fair comparison among techniques. Percolator was chosen for comparison of techniques because it performs the best among all search engines and MS2 strategies. For the analysis of human cell proteome using individual MS2 strategies, the highest number of identifications was achieved by HCD-OT, followed by HCD-IT and CID-IT. Based on these results, we concluded that a relatively fair platform for data interpretation is necessary to avoid divergent conclusions from the same dataset, and HCD-OT and HCD-IT may be preferable for protein/peptide identification using Orbitrap Fusion.
authors.affiliation: Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, United States of America.|New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, United States of America.
authors.forename: Chengjian
authors.initials: C
authors.lastname: Tu
authors.affiliation: Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, United States of America.|New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, United States of America.
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, United States of America.|New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, United States of America.
authors.forename: Shichen
authors.initials: S
authors.lastname: Shen
authors.affiliation: Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, United States of America.
authors.forename: Quanhu
authors.initials: Q
authors.lastname: Sheng
authors.affiliation: Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, United States of America.
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
authors.affiliation: Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, United States of America.|New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, United States of America.
authors.forename: Jun
authors.initials: J
authors.lastname: Qu
mesh_terms: D045744:Cell Line, Tumor; D030562:Databases, Protein; D006801:Humans; D013058:Mass Spectrometry / Q000295:instrumentation* / Q000592:standards; D040901:Proteomics