Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks

Masahiro Sugimoto, Shinichi Kikuchi, Masanori Arita, Tomoyoshi Soga, Takaaki Nishioka, Maseru Tomita

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

We developed a computational technique to assist in the huge-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 un characterized CE-MS peaks in metabolome analysis.

Original languageEnglish
Pages (from-to)78-84
Number of pages7
JournalAnalytical chemistry
Volume77
Issue number1
DOIs
Publication statusPublished - 2005 Jan 1

ASJC Scopus subject areas

  • Analytical Chemistry

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