Research Article

Database search post-processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping

  1. Petr G. Lokhov*,
  2. Olga V. Tikhonova,
  3. Sergei A. Moshkovskii,
  4. Eugene I. Goufman,
  5. Marina V. Serebriakova,
  6. Boris I. Maksimov,
  7. Ilya Yu. Toropyguine,
  8. Victor G. Zgoda,
  9. Vadim M. Govorun,
  10. Alexander I. Archakov

Article first published online: 22 AUG 2003

DOI: 10.1002/pmic.200300580

Issue

PROTEOMICS

PROTEOMICS

Volume 4, Issue 3, pages 633–642, March 2004

Additional Information

How to Cite

Lokhov, P. G., Tikhonova, O. V., Moshkovskii, S. A., Goufman, E. I., Serebriakova, M. V., Maksimov, B. I., Toropyguine, I. Y., Zgoda, V. G., Govorun, V. M. and Archakov, A. I. (2004), Database search post-processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping. PROTEOMICS, 4: 633–642. doi: 10.1002/pmic.200300580

Author Information

  1. Institute of Biomedical Chemistry, Moscow, Russia

*Institute of Biomedical Chemistry, Pogodinskaya st. 10, 119121 Moscow, Russia Fax: +7-095-2450857

Publication History

  1. Issue published online: 19 FEB 2004
  2. Article first published online: 22 AUG 2003
  3. Manuscript Received: 22 FEB 2003

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Keywords:

  • Neural network;
  • Peptide mapping;
  • Protein identification;
  • Protein mixture

Abstract

Database search post-processing by neural network was employed in peptide mapping experiments. The database search was performed using both the known algorithms and score functions, such as Bayesian, MOWSE, Z-score, correlations between calculated and actual peptide length fractional abundance, and, in addition, the probability of protein digest pattern in peptide fingerprint, all embedded in locally developed program. The new signal-processing algorithm based on neural network improves signal-noise separation and is acceptable for automatic protein identification in mixtures. Its power was tested on Helicobacter pylori protein inventory after preceding protein separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Increase in protein identification success rate was observed, and about 100 proteins were identified with no need of human participation in database search estimation.

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