MS2Query: reliable and scalable MS2 mass spectra-based analogue search

The authors develop a machine learning approach to find structurally related chemicals in mass spectral libraries. Their method boosts the annotation rate and aids in assessing novelty in metabolomics datasets. Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.

Automated summary

The authors have developed a machine learning approach, MS2Query, to facilitate chemical discovery in mass spectral libraries. This tool increases the annotation rate and aids in assessing novelty in metabolomics datasets. By integrating mass spectral embedding-based chemical similarity predictors and detected precursor masses, MS2Query offers a more reliable and efficient alternative for searching structurally related molecules in metabolomics studies.