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ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum
Author(s) -
Epstein Jonathan A.,
Blank Paul S.,
Searle Brian C.,
Catlin Aaron D.,
Cologna Stephanie M.,
Olson Matthew T.,
Backlund Peter S.,
Coorssen Jens R.,
Yergey Alfred L.
Publication year - 2016
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201600137
Subject(s) - database search engine , mass spectrometry , fragmentation (computing) , probabilistic logic , computer science , peptide , identification (biology) , peptide mass fingerprinting , mass spectrum , matching (statistics) , data mining , chemistry , search engine , proteomics , chromatography , artificial intelligence , biology , mathematics , information retrieval , statistics , biochemistry , gene , botany , operating system
Current approaches to protein identification rely heavily on database matching of fragmentation spectra or precursor peptide ions. We have developed a method for MALDI TOF‐TOF instrumentation that uses peptide masses and their measurement errors to confirm protein identifications from a first pass MS/MS database search. The method uses MS1‐level spectral data that have heretofore been ignored by most search engines. This approach uses the distribution of mass errors of peptide matches in the MS1 spectrum to develop a probability model that is independent of the MS/MS database search identifications. Peptide mass matches can come from both precursor ions that have been fragmented as well as those that are tentatively identified by accurate mass alone. This additional corroboration enables us to confirm protein identifications to MS/MS‐based scores that are otherwise considered to be only of moderate quality. Straightforward and easily applicable to current proteomic analyses, this tool termed “ProteinProcessor” provides a robust and invaluable addition to current protein identification tools.