Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics.

ABSTRACT: 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 ann

SUBMITTER: Hassanpour N 

PROVIDER: S-EPMC7241244 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature