Electrospray tandem mass spectrometry (ESI-MS/MS) is commonly used in high throughput metabolomics. One of the key obstacles to the effective use of this technology is the difficulty in interpreting measured spectra to accurately and efficiently identify metabolites. Traditional methods for automated metabolite identification compare the target MS or MS/MS spectrum to the spectra in a reference database, ranking candidates based on the closeness of the match. However the limited coverage of available databases has led to an interest in computational methods for predicting reference MS/MS spectra from chemical structures. This work proposes a probabilistic generative model for the MS/MS fragmentation process, which we call competitive fragmentation modeling (CFM), and a machine learning approach for learning parameters for this model from MS/MS data. We show that CFM can be used in both a MS/MS spectrum prediction task (ie, predicting the mass spectrum from a chemical structure), and in a putative metabolite identification task (ranking possible structures for a target MS/MS spectrum). In the MS/MS spectrum prediction task, CFM shows significantly improved performance when compared to a full enumeration of all peaks corresponding to substructures of the molecule. In the metabolite identification task, CFM obtains substantially better rankings for the correct candidate than existing methods (MetFrag and FingerID) on tripeptide and metabolite data, when querying PubChem or KEGG for candidate structures of similar mass.
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Although mass spectrometry measures mass over charge, we assume charge is always 1 (see Assumption 1 in Sect. 2.1.1) and hence can use the mass here.
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Many thanks to Dale Schuurmans, Liang Li, and Jun Peng at the University of Alberta, as well as to the Steinbeck Group at the European Bioinformatics Institute (EMBL-EBI), for invaluable discussions and advice. This work was supported by the Natural Sciences and Engineering Research Council of Canada; Alberta Innovates Technology Futures; and Alberta Innovates Health Solutions and made possible by the Compute Canada Westgrid facility.
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Allen, F., Greiner, R. & Wishart, D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11, 98–110 (2015). https://doi.org/10.1007/s11306-014-0676-4
- Tandem mass spectrometry
- Metabolite identification
- Machine learning