Towards de novo identification of metabolites by analyzing tandem mass spectra

Abstract

Mass spectrometry is among the most widely used technologies in
proteomics and metabolomics. For metabolites, de novo interpretation of
spectra is even more important than for protein data, because metabolite
spectra databases cover only a small fraction of naturally occurring
metabolites. In this work, we analyze a method for fully automated de
novo identification of metabolites from tandem mass spectra.  Mass
spectrometry data is usually assumed to be insufficient for
identification of molecular structures, so we want to estimate the
molecular formula of the unknown metabolite, a crucial step for
its identification. This is achieved by calculating the possible
formulas of the fragment peaks and then reconstructing the most likely
fragmentation tree from this information. We present tests on real mass
spectra showing that our algorithms solve the reconstruction problem
suitably fast and provide excellent results: For all 32 test compounds
the correct solution was among the top five suggestions, for 26
compounds the first suggestion of the exact algorithm was correct.