Fragmentation trees reloaded

TL;DR

This paper introduces a new scoring method for fragmentation trees in tandem mass spectrometry data, significantly improving the identification of unknown metabolites and aiding untargeted metabolomics workflows.

Contribution

A novel scoring approach that transforms fragmentation tree optimization into a maximum a posteriori estimation, enhancing metabolite identification accuracy.

Findings

Outperforms previous scoring methods in molecular formula identification.

Improves database search for structurally similar compounds.

Facilitates automated analysis in untargeted metabolomics.

Abstract

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually relies on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are found by combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. To obtain biochemically meaningful results requires an elaborate optimization function. We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: Both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our methods performs significantly better than the previous scoring, as well as other methods for this task. Our method can expedite the workflow for untargeted metabolomics, allowing researchers to investigate unknowns using automated computational methods.