Research Article

Evaluation of algorithms for protein identification from sequence databases using mass spectrometry data

  1. Daniel C. Chamrad1,
  2. Gerhard Körting1,
  3. Kai Stühler2,
  4. Helmut E. Meyer1,2,
  5. Joachim Klose1,3,
  6. Martin Blüggel1,*

Article first published online: 22 JAN 2004

DOI: 10.1002/pmic.200300612

Issue

PROTEOMICS

PROTEOMICS

Volume 4, Issue 3, pages 619–628, March 2004

Additional Information

How to Cite

Chamrad, D. C., Körting, G., Stühler, K., Meyer, H. E., Klose, J. and Blüggel, M. (2004), Evaluation of algorithms for protein identification from sequence databases using mass spectrometry data. PROTEOMICS, 4: 619–628. doi: 10.1002/pmic.200300612

Author Information

  1. 1

    Protagen, Dortmund, Germany

  2. 2

    Medical Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany

  3. 3

    Institut für Humangenetik, Universitätsklinikum Charité, Berlin, Germany

*Protagen, Emil-Figge-Str. 76 A, D-44227 Dortmund Fax: +49-231-9742-891

Publication History

  1. Issue published online: 19 FEB 2004
  2. Article first published online: 22 JAN 2004
  3. Manuscript Received: 10 APR 2002

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Keywords:

  • Bioinformatics;
  • Database;
  • High-throughput;
  • Mass spectrometry;
  • Protein identification

Abstract

In this work, the commonly used algorithms for mass spectrometry based protein identification, Mascot, MS-Fit, ProFound and SEQUEST, were studied in respect to the selectivity and sensitivity of their searches. The influence of various search parameters were also investigated. Approximately 6600 searches were performed using different search engines with several search parameters to establish a statistical basis. The applied mass spectrometric data set was chosen from a current proteome study. The huge amount of data could only be handled with computational assistance. We present a software solution for fully automated triggering of several peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) algorithms. The development of this high-throughput method made an intensive evaluation based on data acquired in a typical proteome project possible. Previous evaluations of PMF and PFF algorithms were mainly based on simulations.

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