Towards de novo identification of metabolites by analyzing tandem mass spectra

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.