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Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

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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.

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The authors are grateful for the financial support of the National Science Foundation (CBET-0835800). This material is also based upon work supported as part of the Institute for Atom-efficient Chemical Transformations (IACT), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, and Office of Basic Energy Sciences.

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Correspondence to Linda J. Broadbelt.

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Wu, D., Yue, D., You, F. et al. Computational evaluation of factors governing catalytic 2-keto acid decarboxylation. J Mol Model 20, 2310 (2014). https://doi.org/10.1007/s00894-014-2310-9

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  • Catalyst design
  • Decarboxylation
  • Docking
  • Enzyme screening