Seminar, 12. February 2015, Felicity Allen

12. February 2015, 16:15
Ernst-Abbe-Platz 2, seminar room 3423

Competitive Fragmentation Modeling of ESI-MS/MS spectra for putative metabolite identification

Felicity Allen
(Department of Computing Science, University of Alberta)

One of the key obstacles to the effective use of mass spectrometry in high throughput metabolomics 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 interest in computational methods for predicting reference MS/MS spectra from chemical structures. This talk will focus on our recently proposed method for this task, which we call Competitive Fragmentation Modeling (CFM).

This method uses a probabilistic generative model for the MS/MS fragmentation process, and a machine learning approach to learning parameters for this model from MS/MS data. CFM has been 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 showed improved performance when compared to a full enumeration of all peaks corresponding to substructures of the molecule. In a metabolite identification task, CFM obtained 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.