Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.

Abstract

Untargeted mass spectrometry can detect thousands of molecules at once, potentially offering powerful insights into complex samples. However, the increasing scale of experimental datasets and spectral libraries limits our ability to extract and annotate structural information to allow for interpretation. Here, we present the software tool MS2LDA 2.0 that helps to address this gap by identifying recurring fragmentation patterns (Mass2Motifs) that can reflect shared chemical substructures. We introduce automated annotation support through Mass2Motif Annotation Guidance (MAG) that provides suggestions to interpret detected patterns. Our unsupervised pattern mining tool enables the study of much larger datasets with up to 14 times faster analysis than its predecessor. We demonstrate the utility of MS2LDA 2.0 and MAG in applications such as detecting pesticide-related substructures and exploring unknown fungal compounds. Together, these advances make it easier to uncover meaningful chemical patterns in complex data.