A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to E. coli extract where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures overcoming the need of experimental MS and NMR metabolite databases.

A new metabolomics database and query algorithm for the analysis of 13C–1H HSQC spectra is introduced, which unifies NMR spectroscopic information on 555 metabolites from both the Biological Magnetic Resonance Data Bank (BMRB) and Human Metabolome Database (HMDB). The new database, termed Complex Mixture Analysis by NMR (COLMAR) 13C–1H HSQC database, can be queried via an interactive, easy to use web interface at . Our new HSQC database separately treats slowly exchanging isomers that belong to the same metabolite, which permits improved query in cases where lowly populated isomers are below the HSQC detection limit. The performance of our new database and query web server compares favorably with the one of existing web servers, especially for spectra of samples of high complexity, including metabolite mixtures from the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on average a 37% higher accuracy (true positive rate) and a 82% lower false positive rate, which makes it a useful tool for the rapid and accurate identification of metabolites from 13C–1H HSQC spectra at natural abundance. This information can be combined and validated with NMR data from 2D TOCSY-type spectra that provide connectivity information not present in HSQC spectra.

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