Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction

Hanna D. Clements, Autumn R. Flynn, Bryce T. Nicholls, Daria Grosheva, Sarah J. Lefave, Morgan T. Merriman, Todd K. Hyster, Matthew S. Sigman

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an “ene”-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.

Original languageEnglish (US)
Pages (from-to)17656-17664
Number of pages9
JournalJournal of the American Chemical Society
Volume145
Issue number32
DOIs
StatePublished - Aug 16 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Biochemistry
  • Catalysis
  • Colloid and Surface Chemistry

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