Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

Di Wu, Dajun Yue, Fengqi You, Linda J. Broadbelt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

Original languageEnglish (US)
Article number2310
JournalJournal of Molecular Modeling
Volume20
Issue number6
DOIs
StatePublished - Jan 1 2014

Keywords

  • Catalyst design
  • Decarboxylation
  • Docking
  • Enzyme screening

ASJC Scopus subject areas

  • Catalysis
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Computational Theory and Mathematics
  • Inorganic Chemistry

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