Score Model Based on Forward-Reverse Databases for Phosphopeptides Identification

Xiao Zhou Chen

doi:10.4028/www.scientific.net/AMR.898.827

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Score Model Based on Forward-Reverse Databases for Phosphopeptides Identification

Abstract:

Identification of phosphorylated peptides takes into account factors relevant in matching, building models, and score algorithms. In the paper we make a detailed comparative analysis among various phosphorylation identification methods, and study current mainstream algorithms in database searching and identification, and compares various aspects and methods of algorithms in site assessment. Based on the theory of forward-reverse databases searching, It is proposed a new score model to ensure the quality of identification. Our result shows that PTM and Mascot score models were strongly correlated and complementated in their differentiation abilities. Therefore, PTM and Mascot score models can be combined to filter peptide.

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Periodical:

Advanced Materials Research (Volume 898)

Pages:

827-830

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.898.827

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Online since:

February 2014

Authors:

Xiao Zhou Chen*

Keywords:

Forward-Reverse Databases, Phosphopeptides Identification, Score Model

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* - Corresponding Author

References

[1] Garnak, M. Reeves, HCPhosphorylation of isoctrate dehydrofenase of Escherichia coli. Science, 1979, 203 (4385) : 1111-1112.

DOI: 10.1126/science.34215

Google Scholar

[2] Hjerrild, M. Gammeloft, S. Phosphoproteomics toolbox: computational biology, protein chemistry and mass spectrometry. FEBS lett., 2006, 580: 4764-4770.

DOI: 10.1016/j.febslet.2006.07.068

Google Scholar

[3] Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature, 2003, 422: 198-207.

DOI: 10.1038/nature01511

Google Scholar

[4] Gygi, SPCorthals, GLZhang, Y. Rochon, Y. & Aebersold, R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Aced. Sci. USA, 2000, 97: 9390-9395.

DOI: 10.1073/pnas.160270797

Google Scholar

[5] Thingholm TE, Jensen ON, Larsen MR. Analytical strategies for phosphoproteomics. proteomics 2009, 9: 1451-1468.

DOI: 10.1002/pmic.200800454

Google Scholar

[6] David N. Perkins, Darryl JCPappin, David M. Creasy, John S. Cottrell. Probability-based protein identification by searching sequence databases using mass spectrometry data. 1999 , 20: 3551-3567.

DOI: 10.1002/(sici)1522-2683(19991201)20:18<3551::aid-elps3551>3.0.co;2-2

Google Scholar

[7] Jimmy K. Eng, Ashley L. McCormack, John R. Yates III. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. 1994, 5: 976-989.

DOI: 10.1016/1044-0305(94)80016-2

Google Scholar

[8] Jseper V. Olsen, Matthias Mann. Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation [J]. PNAS , 2004, 101: 13417-13422.

DOI: 10.1073/pnas.0405549101

Google Scholar

[9] Sean A Beausoleil, Judit Villen, Scott A Gerber, John Rush, Steven P Gygi. A probability-based approach for high-throughput protein phosphorylation analysis and site localization [J]. Nature biotechnology , 2006, 24: 1285-1292.

DOI: 10.1038/nbt1240

Google Scholar

[10] Wifred H. Tang, Ignat V. Shilov, Sean L. Seymour. Nonlinear Fitting Method for Determining Local False Discovery Rates from Decoy Database Searches [J]. Journal of Proteome Research, 2008, 7: 3661-3667.

DOI: 10.1021/pr070492f

Google Scholar