Metabolic in silico network expansions to predict and exploit enzyme promiscuity

James Jeffryes, Jonathan Strutz, Chris Henry, Keith E.J. Tyo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

There is a growing consensus that enzymes are capable of catalyzing not just one canonical reaction but entire families of related reactions. These capacities often go unnoticed in the enzyme’s native context but can become apparent in engineered metabolism when the enzyme is exposed to novel substrates or high concentrations of pathway intermediates. This chapter describes how to use metabolic in silico network expansion (MINE) databases to predict novel biotransformations and their resulting metabolites. In particular, searching MINEs by structural similarity or with metabolomics data allows scientists to detect, exploit, or avoid these predicted transformations.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages11-21
Number of pages11
DOIs
StatePublished - 2019

Publication series

NameMethods in Molecular Biology
Volume1927
ISSN (Print)1064-3745

Keywords

  • Enzyme promiscuity
  • Feature annotation
  • Metabolite damage
  • Untargeted metabolomics

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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