Database search post‐processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping
Petr G. Lokhov, Olga V. Tikhonova, Sergei A. Moshkovskii, Eugene Goufman, Marina V. Serebryakova, Boris I. Maksimov, Ilya Yu. Toropyguine, Victor Gavrilovich Zgoda, Vadim Markovich Govorun, Alexander I. Archakov · PROTEOMICS · 2004
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.