KEGG LIGAND Research Articles

Large-Scale Prediction of Cationic Metabolite Identity and Migration Time in Capillary Electrophoresis Mass Spectrometry Using Artificial Neural Networks

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.

DATABASE: Biochemistry's Little Guys

DNA, RNA, and proteins are the big shots, but cells also depend on a host of smaller compounds and atoms such as zinc. To help drug designers, molecular biologists, and other researchers quickly round up information on these often overlooked components, the European Bioinformatics Institute (EBI) has launched a new database called ChEBI. The collection tallies more than 2700 natural and synthetic “small molecular entities”: atoms, molecules, free radicals, ions, and other chemicals with biological impact. You can nab data such as the entry's chemical classification, function, and alternate names. Uncover its structure and what biochemical pathways it participates in by connecting to the KEGG Ligand Database (NetWatch, 3 April 1998, p. 7). To learn what proteins each example interacts with, follow links to EBI's UniProt database, a compendium of sequence and function information.