Literature DB >> 19368407

Mascot-derived false positive peptide identifications revealed by manual analysis of tandem mass spectra.

Yue Chen1, Junmei Zhang, Gang Xing, Yingming Zhao.   

Abstract

False positives that arise when MS/MS data are used to search protein sequence databases remain a concern in proteomics research. Here, we present five types of false positives identified when aligning sequences to MS/MS spectra by Mascot database searching software. False positives arise because of (1) enzymatic digestion at abnormal sites; (2) misinterpretation of charge states; (3) misinterpretation of protein modifications; (4) incorrect assignment of the protein modification site; and (5) incorrect use of isotopic peaks. We present examples, clearly identified as false positives by manual inspection, that nevertheless were assigned high scores by Mascot sequence alignment algorithm. In some examples, the sequence assigned to the MS/MS spectrum explains more than 80% of the fragment ions present. Because of high sequence similarity between the false positives and their corresponding true hits, the false positive rate cannot be evaluated by the common method of using a reversed or scrambled sequence database. A common feature of the false positives is the presence of unmatched peaks in the MS/MS spectra. Our studies highlight the importance of using unmatched peaks to remove false positives and offer direction to aid development of better sequence alignment algorithms for peptide and PTM identification.

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Year:  2009        PMID: 19368407      PMCID: PMC2720604          DOI: 10.1021/pr900172v

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  28 in total

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2.  Peptide sequence motif analysis of tandem MS data with the SALSA algorithm.

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

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5.  Intensity-based protein identification by machine learning from a library of tandem mass spectra.

Authors:  Joshua E Elias; Francis D Gibbons; Oliver D King; Frederick P Roth; Steven P Gygi
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6.  Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data.

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Journal:  Proteomics       Date:  2003-06       Impact factor: 3.984

7.  A method for reducing the time required to match protein sequences with tandem mass spectra.

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Journal:  J Proteome Res       Date:  2008-01-26       Impact factor: 4.466

9.  Error-tolerant identification of peptides in sequence databases by peptide sequence tags.

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10.  Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

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  17 in total

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Review 2.  Ubiquitinated proteome: ready for global?

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4.  Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides.

Authors:  Boris Bogdanow; Henrik Zauber; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2016-05-23       Impact factor: 5.911

Review 5.  A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.

Authors:  Alexey I Nesvizhskii
Journal:  J Proteomics       Date:  2010-09-08       Impact factor: 4.044

6.  Integrated data management and validation platform for phosphorylated tandem mass spectrometry data.

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Journal:  Proteomics       Date:  2010-10       Impact factor: 3.984

7.  The bacterial arginine glycosyltransferase effector NleB preferentially modifies Fas-associated death domain protein (FADD).

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8.  Determining the Mitochondrial Methyl Proteome in Saccharomyces cerevisiae using Heavy Methyl SILAC.

Authors:  Katelyn E Caslavka Zempel; Ajay A Vashisht; William D Barshop; James A Wohlschlegel; Steven G Clarke
Journal:  J Proteome Res       Date:  2016-10-18       Impact factor: 4.466

9.  MS/MS/MS reveals false positive identification of histone serine methylation.

Authors:  Junmei Zhang; Yue Chen; Zhihong Zhang; Gang Xing; Joanna Wysocka; Yingming Zhao
Journal:  J Proteome Res       Date:  2010-01       Impact factor: 4.466

Review 10.  Quantifying oncogenic phosphotyrosine signaling networks through systems biology.

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Journal:  Curr Opin Genet Dev       Date:  2010-01-13       Impact factor: 5.578

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