Identifying the unknowns: towards structural elucidation of small molecules using mass spectrometry

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Rapid identification of small compounds from small amounts of substance is of interest in many areas of biology and medicine such as metabolomics, drug discovery, biomarker discovery, and diagnostics. Today, mass spectrometry (MS) is a key technology for the identification of small molecules. Due to immense technological advances in mass spectrometry over the last years, the amount and complexity of MS data has been growing rapidly. Computational analysis of such data is presumed to be one of the major technological hurdles in metabolomics today. We proposed fragmentation trees (FTs) as a model for the fragmentation reactions in the measurement.In the first two years of the project, we have improved the FT calculation, extended the fragmentation tree concept to multistage mass spectrometry, and developed a method to predict fragmentation trees from high resolution GC-MS data. Most importantly, we developed a concept to align such fragmentation trees. The resulting alignment scores were used in various applications, among them the database similarity search tool FT-BLAST. FT alignments outperformed standard spectral comparison in these applications.In the following two years, we will continue to improve the quality of FTs from several analytical platforms as well as the quality of the alignments. We will develop statistically meaningful scores for both the calculation and alignment of FTs. We will provide an internet platform so that fragmentation trees and losses can be evaluated by MS experts. We will construct biochemical reaction networks from MS data. Finally, we will make the developed methods available to mass spectrometrists by an easy-to-use software tool.