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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
Author(s) -
Lokhov Petr G.,
Tikhonova Olga V.,
Moshkovskii Sergei A.,
Goufman Eugene I.,
Serebriakova Marina V.,
Maksimov Boris I.,
Toropyguine Ilya Yu.,
Zgoda Victor G.,
Govorun Vadim M.,
Archakov Alexander I.
Publication year - 2004
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200300580
Subject(s) - database , artificial neural network , database search engine , peptide , fingerprint (computing) , computer science , pattern recognition (psychology) , identification (biology) , artificial intelligence , data mining , chemistry , biology , search engine , biochemistry , information retrieval , botany
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.