title: Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.
abstract: We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sugimoto
authors.forename: Shinichi
authors.initials: S
authors.lastname: Kikuchi
authors.forename: Masanori
authors.initials: M
authors.lastname: Arita
authors.forename: Tomoyoshi
authors.initials: T
authors.lastname: Soga
authors.forename: Takaaki
authors.initials: T
authors.lastname: Nishioka
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D002412:Cations / Q000032:analysis*; D019075:Electrophoresis, Capillary / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D016571:Neural Networks, Computer
chemical_list: D002412:Cations