4.8 Article

Optimization of Enzyme Mechanism along the Evolutionary Trajectory of a Computationally Designed (Retro-)Aldolase

Journal

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 139, Issue 36, Pages 12541-12549

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/jacs.7b05796

Keywords

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Funding

  1. ETH Zurich
  2. Swiss National Science Foundation
  3. Marie Sklodowska-Curie Individual Fellowship (TIMEnzyme)
  4. Stipendienfonds der Schweizerischen Chemischen Industrie (SSCI)

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De novo biocatalysts have been successfully generated by computational design and subsequent experimental optimization. Here, we examined the evolutionary history of the computationally designed (retro-)aldolase RA9S. The modest activity of the starting enzyme was previously improved 10(5)-fold over many rounds of mutagenesis and screening to afford a proficient biocatalyst for enantioselective cleavage and synthesis of beta-hydroxyketones. Using a set of representative RA9S variants, we probed individual steps in the multistep reaction pathway to determine which processes limit steady-state turnover and how mutations that accumulated along the evolutionary trajectory influenced the kinetic mechanism. We found that the overall rate-limiting step for aldol cleavage shifted from C-C bond scission (or an earlier step in the pathway) for the computational design to product release for the evolved enzymes. Specifically, interconversion of Schiff base and enamine intermediates, formed covalently between acetone and the catalytic lysine residue, was found to be the slowest step for the most active variants. A complex hydrogen bond network of four active site residues, which was installed in the late stages of laboratory evolution, apparently enhances lysine reactivity and facilitates efficient proton shuffling. This catalytic tetrad accounts for the tremendous rate acceleration observed for all steps of the mechanism, most notably Schiff base formation and hydrolysis. Comparison of our results with kinetic and structural studies on natural aldolases provides valuable feedback for computational enzyme design and laboratory evolution approaches alike.

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