Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification

By Felicity Allen, Russ Greiner, David S. Wishart. Metabolomics, 2013

385 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/beb51ca050d0ef141a4520b2e6c833bc13098c39

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Citations in the corpus

Searching molecular structure databases with tandem mass spectra using CSI:FingerID (2015, background): “For the general case, several strategies have been proposed during recent years, including simulation of mass spectra from molecular structure (10, 11), combinatorial fragmentation (12 – 17), and prediction of molecular fingerprints (18, 19).”
CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra (2014, methodology): “…the algorithm computes the predicted spectrum; it uses the marginal peak probabilities to determine the peak intensities, and presents only the top 80% of the total intensity sum, within the limits of 5 and 30 peaks, as described in a more detailed manuscript covering the CFM algorithm (8).”
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, methodology): “Machine learning-based methods such as CFM-ID developed by Allen et al. allow for the computation of CID-MS/MS [70] and EI-MS spectra [71] directly from molecular structures.”
MetFrag relaunched: incorporating strategies beyond in silico fragmentation (2016, methodology): “The expected ranks, which partially account for equally scored candidates as calculated in [16], are shown in the lower part of the table MetFrag2010 MetFrag2.”