Literature DB >> 27546229

ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

Jonathan A Epstein1, Paul S Blank2, Brian C Searle3, Aaron D Catlin1, Stephanie M Cologna4, Matthew T Olson5, Peter S Backlund1, Jens R Coorssen6, Alfred L Yergey7.   

Abstract

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.
© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Bayesian analysis; Bioinformatics; MALDI; Protein sequencing; Tandem mass spectrometry

Mesh:

Year:  2016        PMID: 27546229      PMCID: PMC5176252          DOI: 10.1002/pmic.201600137

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  13 in total

1.  Probability-based protein identification by searching sequence databases using mass spectrometry data.

Authors:  D N Perkins; D J Pappin; D M Creasy; J S Cottrell
Journal:  Electrophoresis       Date:  1999-12       Impact factor: 3.535

2.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

3.  A statistical model for identifying proteins by tandem mass spectrometry.

Authors:  Alexey I Nesvizhskii; Andrew Keller; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2003-09-01       Impact factor: 6.986

4.  Rapid identification of proteins by peptide-mass fingerprinting.

Authors:  D J Pappin; P Hojrup; A J Bleasby
Journal:  Curr Biol       Date:  1993-06-01       Impact factor: 10.834

5.  Software Analysis of Uncorrelated MS1 Peaks for Discovery of Post-Translational Modifications.

Authors:  Bruce D Pascal; Graham M West; Catherina Scharager-Tapia; Ricardo Flefil; Tina Moroni; Pablo Martinez-Acedo; Patrick R Griffin; Anthony C Carvalloza
Journal:  J Am Soc Mass Spectrom       Date:  2015-08-12       Impact factor: 3.109

6.  The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 study: relative protein quantitation.

Authors:  Christoph W Turck; Arnold M Falick; Jeffrey A Kowalak; William S Lane; Kathryn S Lilley; Brett S Phinney; Susan T Weintraub; H Ewa Witkowska; Nathan A Yates
Journal:  Mol Cell Proteomics       Date:  2007-05-18       Impact factor: 5.911

7.  Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

Authors:  M P Washburn; D Wolters; J R Yates
Journal:  Nat Biotechnol       Date:  2001-03       Impact factor: 54.908

8.  Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases.

Authors:  W J Henzel; T M Billeci; J T Stults; S C Wong; C Grimley; C Watanabe
Journal:  Proc Natl Acad Sci U S A       Date:  1993-06-01       Impact factor: 11.205

9.  Quantitative proteomics analysis of inborn errors of cholesterol synthesis: identification of altered metabolic pathways in DHCR7 and SC5D deficiency.

Authors:  Xiao-Sheng Jiang; Peter S Backlund; Christopher A Wassif; Alfred L Yergey; Forbes D Porter
Journal:  Mol Cell Proteomics       Date:  2010-03-19       Impact factor: 5.911

10.  Quantitative proteomic analysis of Niemann-Pick disease, type C1 cerebellum identifies protein biomarkers and provides pathological insight.

Authors:  Stephanie M Cologna; Xiao-Sheng Jiang; Peter S Backlund; Celine V M Cluzeau; Michelle K Dail; Nicole M Yanjanin; Stephan Siebel; Cynthia L Toth; Hyun-sik Jun; Christopher A Wassif; Alfred L Yergey; Forbes D Porter
Journal:  PLoS One       Date:  2012-10-29       Impact factor: 3.240

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