Literature DB >> 30788782

Metabolic In Silico Network Expansions to Predict and Exploit Enzyme Promiscuity.

James Jeffryes1,2, Jonathan Strutz1, Chris Henry2, Keith E J Tyo3.   

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.

Keywords:  Enzyme promiscuity; Feature annotation; Metabolite damage; Untargeted metabolomics

Mesh:

Substances:

Year:  2019        PMID: 30788782     DOI: 10.1007/978-1-4939-9142-6_2

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  BioTransformer 3.0-a web server for accurately predicting metabolic transformation products.

Authors:  David S Wishart; Siyang Tian; Dana Allen; Eponine Oler; Harrison Peters; Vicki W Lui; Vasuk Gautam; Yannick Djoumbou-Feunang; Russell Greiner; Thomas O Metz
Journal:  Nucleic Acids Res       Date:  2022-05-10       Impact factor: 19.160

Review 2.  Endogenous toxic metabolites and implications in cancer therapy.

Authors:  Namgyu Lee; Meghan E Spears; Anne E Carlisle; Dohoon Kim
Journal:  Oncogene       Date:  2020-07-24       Impact factor: 9.867

  2 in total

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