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Kemp elimination catalysts by computational enzyme design

Author

Listed:
  • Daniela Röthlisberger

    (Department of Biochemistry,)

  • Olga Khersonsky

    (and)

  • Andrew M. Wollacott

    (Department of Biochemistry,)

  • Lin Jiang

    (Department of Biochemistry,
    Biomolecular Structure and Design, and,)

  • Jason DeChancie

    (University of California, Los Angeles, California 90095, USA)

  • Jamie Betker

    (Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA)

  • Jasmine L. Gallaher

    (Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA)

  • Eric A. Althoff

    (Department of Biochemistry,)

  • Alexandre Zanghellini

    (Department of Biochemistry,
    Biomolecular Structure and Design, and,)

  • Orly Dym

    (Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot 76100, Israel)

  • Shira Albeck

    (Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot 76100, Israel)

  • Kendall N. Houk

    (University of California, Los Angeles, California 90095, USA)

  • Dan S. Tawfik

    (and)

  • David Baker

    (Department of Biochemistry,
    Biomolecular Structure and Design, and,
    Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA)

Abstract

The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination—a model reaction for proton transfer from carbon—with measured rate enhancements of up to 105 and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a >200-fold increase in kcat/Km (kcat/Km of 2,600 M-1s-1 and kcat/kuncat of >106). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.

Suggested Citation

  • Daniela Röthlisberger & Olga Khersonsky & Andrew M. Wollacott & Lin Jiang & Jason DeChancie & Jamie Betker & Jasmine L. Gallaher & Eric A. Althoff & Alexandre Zanghellini & Orly Dym & Shira Albeck & K, 2008. "Kemp elimination catalysts by computational enzyme design," Nature, Nature, vol. 453(7192), pages 190-195, May.
    • Handle: RePEc:nat:nature:v:453:y:2008:i:7192:d:10.1038_nature06879
    DOI: 10.1038/nature06879
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    Cited by:

    1. Julian Nazet & Elmar Lang & Rainer Merkl, 2021. "Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-23, August.

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