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Reinterpretation of the protein identification process for proteomics data

  • Kwon, Kyung-Hoon (Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Chungbuk, Republic of Korea) ;
  • Lee, Sang-Kwang (Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Chungbuk, Republic of Korea) ;
  • Cho, Kun (Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Chungbuk, Republic of Korea) ;
  • Park, Gun-Wook (Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Chungbuk, Republic of Korea) ;
  • Kang, Byeong-Soo (The I-BIO graduate program and National Core Research Center for Systems Bio-Dynamics, POSTECH) ;
  • Park, Young-Mok (Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang, Chungbuk, Republic of Korea)
  • Published : 2009.09.30

Abstract

Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify the protein as the matched sequence. The match score is defined to find the matches from the database and declare the highest scored hit as the most probable protein. According to the score definition, search result varies. In this study, the difference among search results of different search engines or different databases was investigated, in order to suggest a better way to identify more proteins with higher reliability. Materials and Methods: The protein extract of human mesenchymal stem cell was separated by several bands by one-dimensional electrophorysis. One-dimensional gel was excised one by one, digested by trypsin and analyzed by a mass spectrometer, FT LTQ. The tandem mass (MS/MS) spectra of peptide ions were applied to the database search of X!Tandem, Mascot and Sequest search engines with IPI human database and SwissProt database. The search result was filtered by several threshold probability values of the Trans-Proteomic Pipeline (TPP) of the Institute for Systems Biology. The analysis of the output which was generated from TPP was performed. Results and Discussion: For each MS/MS spectrum, the peptide sequences which were identified from different conditions such as search engines, threshold probability, and sequence database were compared. The main difference of peptide identification at high threshold probability was caused by not the difference of sequence database but the difference of the score. As the threshold probability decreases, the missed peptides appeared. Conversely, in the extremely high threshold level, we missed many true assignments. Conclusion and Prospects: The different identification result of the search engines was mainly caused by the different scoring algorithms. Usually in proteomics high-scored peptides are selected and low-scored peptides are discarded. Many of them are true negatives. By integrating the search results from different parameter and different search engines, the protein identification process can be improved.

Keywords

References

  1. Alves, G., Wu, W.W., Wang, G., Shen, R.F. and Yu, Y.K. (2008) Enhancing peptide identification confidence by combining search methods. J. Proteome Res. 7(8), 3102-13 https://doi.org/10.1021/pr700798h
  2. Craig, R., Beavis, R.C. (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466-1467 https://doi.org/10.1093/bioinformatics/bth092.
  3. Dancik, V., Addona, T.A., Clauser, K.R., Vath, J.E. and Pevzner, P.A. (1999) De Novo Peptide Sequencing via Tandem Mass Spectrometry. J. Comp. Biol. 6, 327-342 https://doi.org/10.1089/106652799318300
  4. Elias, J.E., Gibbons, F.D., King, O.D., Roth, F.P. and Gygi, S.P. (2004) Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nat. Biotech. 22, 214-219 https://doi.org/10.1038/nbt930
  5. Eng, J.K., McCormack, A.L., Yates, JR III (1994) An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database. J. Am. Soc. Mass Spectrom 5, 976-989 https://doi.org/10.1016/1044-0305(94)80016-2
  6. Eng, J.K., Fischer, B., Grossmann, J. and MacCoss, M.J. (2008) A Fast SEQUEST Cross Correlation Algorithm. J. Proteome Res. 7, 4598-4602 https://doi.org/10.1021/pr800420s
  7. Geer, L.Y., Markey, S.P., Kowalak, J.A., Wagner, L., Xu, M., Maynard, D.M., Yang, X., Shi, W. and Bryant, S.H. (2004) Open mass spectrometry search algorithm, J. Proteome Res. 3(5), 958-64 https://doi.org/10.1021/pr0499491
  8. Keller, A., Nesvizhskii, A.I., Kolker, E. and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392 https://doi.org/10.1021/ac025747h
  9. Keller ,A., Eng, J., Zhang, N., Li, X. and Aebersold, R. (2005) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Sys. Biol. 2, 1-8 https://doi.org/10.1038/msb4100024
  10. Nesvizhskii, A.I., Keller, A., Kolker, E. and Aebersold, R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646-4658 https://doi.org/10.1021/ac0341261
  11. Kapp, E.A., Schutz, F., Connolly, L.M., Chakel, J.A., Meza, J.E., Miller, C.A., Fenyo, D., Eng, J.K., Adkins, J.N., and Omenn, G,S. (2005) An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis. Proteomics 5, 3475-90 https://doi.org/10.1002/pmic.200500126
  12. Kersey, P.J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E. and Apweiler, R. (2004) The International Protein Index : an integrated database for proteomics experiments, Proteomics, 4(7), 1985-8 https://doi.org/10.1002/pmic.200300721
  13. O'Donovan, C., Martin, M.J., Gattiker, A., Gastelger, E., Bairoch, A. and Apweiler, R. (2002) High-quality protein knowledge resource: SWISS-PROT and TrEMBL. Brief Bioinform. 3(3), 275-84 https://doi.org/10.1093/bib/3.3.275
  14. Omenn, G.S., States, D.J., et al. (2005) Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database, Proteomics 5(13), 3226-45 https://doi.org/10.1002/pmic.200500358
  15. Perkins, D.N., Pappin, D.J.C., Creasy, D.M. and Cottrell, J.S. (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551-3567 https://doi.org/10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2