Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.

Rene Boiteau,Hannah Kinmonth-Schultz,Carrie Nicora,David Hoyt,Joy Ward,Kerem Bingol

doi:10.3390/metabo8010008

Abstract

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

Highlights

Metabolomics has become a key discipline in small molecule research
Our Nuclear magnetic resonance (NMR)/mass spectrometry (MS)-based approach works as follows: we first determine the chemical formula of an unknown LC-MS feature by analyzing its accurate mass and isotopic distributions
We compare the predicted MS/MS and NMR data against the experimental MS/MS and NMR, and rank them according to the level of agreement to determine the best matching candidate

Summary

Introduction

Metabolomics has become a key discipline in small molecule research. It has been widely applied to generate new hypotheses for unresolved scientific questions, such as in metabolism-related disorders, environmental carbon cycling studies, complex biofuel analysis, nutritional and food sciences, and synthetic biology optimization [1,2,3,4,5]. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most powerful experimental methods for metabolomics [6,7]. This is primarily because of their high-resolution powers providing detection of individual molecular species in a short acquisition time with minimal sample preparation. Connecting the detected signals to scientific interpretation requires assignment of individual signals to their corresponding metabolite identities This is often performed by querying detected signals against one or several experimental databases such as BMRB [8], HMDB [9], METLIN [10] and COLMAR [11]. Excellent progress has been made in growing the number of metabolites in databases, many of the signals detected in metabolomics experiments cannot be identified through this database approach because of the absence of their spectra in metabolomics databases

Methods

Results

Conclusion