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

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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BibTeXRIS

Petr G Lokhov, Olga V Tikhonova, Sergei A Moshkovskii, Eugene I Goufman, Marina V Serebriakova, Boris I Maksimov, Ilya Yu Toropyguine, Victor G Zgoda, Vadim M Govorun, Alexander I Archakov. 2004. Database search post-processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping.. https://doi.org/10.1002/pmic.200300580

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