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Area of Science:

  • Metabolomics
  • Computational Chemistry
  • Bioinformatics

Background:

  • Tandem mass spectrometry (MS/MS) is crucial for metabolite structural annotation, but identification relies heavily on spectral databases, limiting scope.
  • Metabolites are structurally related through bioreactions, suggesting related MS/MS spectra, yet exploring this link is challenging.

Purpose of the Study:

  • To develop a deep-learning method (DeepMASS) for scoring structural similarity between metabolites using MS/MS spectra.
  • To enable identification of unknown metabolites by relating them to known compounds via their spectral data.

Main Methods:

  • Implemented a deep-learning model, DeepMASS, to analyze MS/MS spectra and predict structural similarity.
  • Validated DeepMASS using leave-one-out cross-validation on 662 KEGG compounds and an external dataset from a male infertility study.

Main Results:

  • DeepMASS effectively scores structural similarity between metabolites based on MS/MS spectra.
  • The study demonstrated that identifying unknown compounds is feasible when structurally related metabolites are present in databases.

Conclusions:

  • DeepMASS offers an effective strategy to extend metabolite identification capabilities beyond current MS/MS spectral database limitations.
  • This approach enhances the scope and accuracy of metabolomic structural annotation by inferring relationships between known and unknown compounds.