Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics. Academic Article uri icon


  • Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.

published proceedings

  • Metabolites

altmetric score

  • 0.25

author list (cited authors)

  • Hassanpour, N., Alden, N., Menon, R., Jayaraman, A., Lee, K., & Hassoun, S.

citation count

  • 7

complete list of authors

  • Hassanpour, Neda||Alden, Nicholas||Menon, Rani||Jayaraman, Arul||Lee, Kyongbum||Hassoun, Soha

publication date

  • April 2020