MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra


Autoři: Youzhong Liu aff001;  Aida Mrzic aff001;  Pieter Meysman aff001;  Thomas De Vijlder aff003;  Edwin P. Romijn aff003;  Dirk Valkenborg aff004;  Wout Bittremieux aff001;  Kris Laukens aff001
Působiště autorů: Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium aff001;  Biomedical Informatics Network Antwerpen (biomina), University of Antwerp, Antwerp, Belgium aff002;  Pharmaceutical Development & Manufacturing Sciences (PDMS), Janssen Research & Development, Beerse, Belgium aff003;  Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium aff004;  Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, CA, United States of America aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226770

Souhrn

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.

Klíčová slova:

Drug metabolism – Machine learning algorithms – Mass spectra – Metabolic networks – Metabolites – Metabolomics – Molecular structure – Statistical data


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Článek vyšel v časopise

PLOS One


2020 Číslo 1