Precise Protein Quantification Based on Peptide Quantification Using ITRAQ

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2007 Jun 23

PMID 17584939

Citations 33

Authors

Andreas M Boehm

Stephanie Putz

Daniela Altenhofer

Albert Sickmann

Michael Falk

Affiliations

Soon will be listed here.

Abstract

Background: Mass spectrometry based quantification of peptides can be performed using the iTRAQ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.

Results: A method comprising sound error estimation and statistical methods is presented that allows precise abundance analysis plus error calculation at the peptide as well as at the protein level. This yields the relevant information that is required for quantitative proteomics. Comparing the performance of our method named Quant with existing approaches the error estimation is reliable and offers information for precise bioinformatic models. Quant is shown to generate results that are consistent with those produced by ProQuant, thus validating both systems. Moreover, the results are consistent with that of Mascot 2.2. The MATLAB scripts of Quant are freely available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/, each under the GNU Lesser General Public License.

Conclusion: The software Quant demonstrates improvements in protein quantification using iTRAQ. Precise quantification data can be obtained at the protein level when using error propagation and adequate visualization. Quant integrates both and additionally provides the possibility to obtain more reliable results by calculation of wise quality measures. Peak area integration has been replaced by sum of intensities, yielding more reliable quantification results. Additionally, Quant allows the combination of quantitative information obtained by iTRAQ with peptide and protein identifications from popular tandem MS identification tools. Hence Quant is a useful tool for the proteomics community and may help improving analysis of proteomic experimental data. In addition, we have shown that a lognormal distribution fits the data of mass spectrometry based relative peptide quantification.

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References

Patwardhan A, Strittmatter E, Camp 2nd D, Smith R, Pallavicini M . Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy. Proteomics. 2006; 6(9):2903-15. DOI: 10.1002/pmic.200500582. View

Kolkman A, Daran-Lapujade P, Fullaondo A, Olsthoorn M, Pronk J, Slijper M . Proteome analysis of yeast response to various nutrient limitations. Mol Syst Biol. 2006; 2:2006.0026. PMC: 1681501. DOI: 10.1038/msb4100069. View

Shadforth I, Dunkley T, Lilley K, Bessant C . i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics. 2005; 6:145. PMC: 1276793. DOI: 10.1186/1471-2164-6-145. View

Wilkins M, Sanchez J, Gooley A, Appel R, Humphery-Smith I, Hochstrasser D . Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996; 13:19-50. DOI: 10.1080/02648725.1996.10647923. View

Aebersold R, Goodlett D . Mass spectrometry in proteomics. Chem Rev. 2001; 101(2):269-95. DOI: 10.1021/cr990076h. View

Boehm A, Grosse-Coosmann F, Sickmann A . Command line tool for calculating theoretical MS spectra for given sequences. Bioinformatics. 2004; 20(16):2889-91. DOI: 10.1093/bioinformatics/bth328. View

Boeckmann B, Bairoch A, Apweiler R, Blatter M, Estreicher A, Gasteiger E . The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 2003; 31(1):365-70. PMC: 165542. DOI: 10.1093/nar/gkg095. View

Boehm A, Sickmann A . A comprehensive dictionary of protein accession codes for complete protein accession identifier alias resolving. Proteomics. 2006; 6(15):4223-6. DOI: 10.1002/pmic.200600018. View

Ross P, Huang Y, Marchese J, Williamson B, Parker K, Hattan S . Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004; 3(12):1154-69. DOI: 10.1074/mcp.M400129-MCP200. View

10.

Gygi S, Rist B, Gerber S, Turecek F, Gelb M, Aebersold R . Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol. 1999; 17(10):994-9. DOI: 10.1038/13690. View

11.

Cavalieri D, De Filippo C . Bioinformatic methods for integrating whole-genome expression results into cellular networks. Drug Discov Today. 2005; 10(10):727-34. DOI: 10.1016/S1359-6446(05)03433-1. View

12.

Ong S, Mann M . Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol. 2006; 1(5):252-62. DOI: 10.1038/nchembio736. View

13.

Putz S, Reinders J, Reinders Y, Sickmann A . Mass spectrometry-based peptide quantification: applications and limitations. Expert Rev Proteomics. 2005; 2(3):381-92. DOI: 10.1586/14789450.2.3.381. View

14.

Grosse-Coosmann F, Boehm A, Sickmann A . Efficient analysis and extraction of MS/MS result data from Mascot result files. BMC Bioinformatics. 2005; 6:290. PMC: 1325259. DOI: 10.1186/1471-2105-6-290. View

15.

Boehm A, Galvin R, Sickmann A . Extractor for ESI quadrupole TOF tandem MS data enabled for high throughput batch processing. BMC Bioinformatics. 2004; 5:162. PMC: 535808. DOI: 10.1186/1471-2105-5-162. View

16.

Perkins D, Pappin D, Creasy D, Cottrell J . Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999; 20(18):3551-67. DOI: 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2. View

17.

Eng J, McCormack A, Yates J . An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom. 2013; 5(11):976-89. DOI: 10.1016/1044-0305(94)80016-2. View

18.

Bradford M . A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976; 72:248-54. DOI: 10.1016/0003-2697(76)90527-3. View

19.

MacCoss M, Wu C, Liu H, Sadygov R, Yates 3rd J . A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal Chem. 2003; 75(24):6912-21. DOI: 10.1021/ac034790h. View

20.

Aebersold R, Mann M . Mass spectrometry-based proteomics. Nature. 2003; 422(6928):198-207. DOI: 10.1038/nature01511. View