MetExpert: An expert system to enhance gas chromatography‒mass spectrometry-based metabolite identifications

Abstract

Gas chromatography‒mass spectrometry (GCMS) is an important analytical technique in metabolomics studies and has been routinely used for metabolite profiling of biological samples. Spectral matching to databases of authentic compounds are the preferred tools for metabolite identifications; however, the metabolic coverage of these databases is still limited compared to the number of known metabolites. Several computational tools have been developed to facilitate the interpretation of MS data but unfortunately most of them have limited applicability to GCMS-based metabolite identification. In this paper, we introduce a computer-aided, metabolite expert system called MetExpert which emulates the metabolite-identification ability of a human expert using orthogonal datasets including molecular formulas, retention indices, and EI-MS spectra to characterize the molecular structures. This system integrates four modules including in silico derivatization, metabolite-likeness evaluation, retention prediction, and substructure prediction. In silico derivatization increases the searchable chemical space for TMS-derivatized metabolites many of which are absent in molecular structure databases. Metabolite-likeness evaluations are an efficient approach to select metabolite-like molecules when querying large databases such as PubChem. An artificial neutral network then establishes the quantitative structure‒retention relationships for the accurate prediction of RIs that further refines the candidate molecules. In addition, PLS-DA models establish quantitative structure‒spectra relationships for the prediction of metabolite substructures. Finally, weighted scoring of three orthogonal evaluations increases the identification rates. MetExpert outperformed current state-of-the-art methods such as MetFrag and CFM-ID for ranking the correct identifications. While spectral comparisons with chemical standards or de novo structural elucidations are necessary to validate the predictions, MetExpert provides an efficient and effective approach to prioritize the candidates.

Keywords: Expert system; Gas chromatography‒Mass spectrometry; In silico derivatization; Metabolite identification; Retention prediction; Substructure prediction.