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Computational redesign of endonuclease DNA binding and cleavage specificity

Author

Listed:
  • Justin Ashworth

    (Howard Hughes Medical Institute and Department of Biochemistry)

  • James J. Havranek

    (Howard Hughes Medical Institute and Department of Biochemistry)

  • Carlos M. Duarte

    (Howard Hughes Medical Institute and Department of Biochemistry)

  • Django Sussman

    (Fred Hutchinson Cancer Research Center)

  • Raymond J. Monnat

    (University of Washington)

  • Barry L. Stoddard

    (Fred Hutchinson Cancer Research Center)

  • David Baker

    (Howard Hughes Medical Institute and Department of Biochemistry)

Abstract

Design for living Altering the specificity of DNA-cleaving enzymes could be useful in many medical or biotechnological applications, but it is quite a challenge in terms of computational protein design. Ashwell et al. have used computational redesign to alter the target-site specificity of the I-MsoI homing endonuclease, while maintaining wild-type binding affinity. The redesigned enzyme binds and cleaves the new DNA recognition site about 10,000 times more effectively than the wild-type enzyme, with target discrimination comparable to the original endonuclease. These results suggest that computational protein design methods can be used to create novel and highly specific endonucleases for gene therapy and other applications.

Suggested Citation

  • Justin Ashworth & James J. Havranek & Carlos M. Duarte & Django Sussman & Raymond J. Monnat & Barry L. Stoddard & David Baker, 2006. "Computational redesign of endonuclease DNA binding and cleavage specificity," Nature, Nature, vol. 441(7093), pages 656-659, June.
    • Handle: RePEc:nat:nature:v:441:y:2006:i:7093:d:10.1038_nature04818
    DOI: 10.1038/nature04818
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    Cited by:

    1. Ulaganathan, Kandasamy & Goud, Sravanthi & Reddy, Madhavi & Kayalvili, Ulaganathan, 2017. "Genome engineering for breaking barriers in lignocellulosic bioethanol production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1080-1107.
    2. Zhiqiang Yan & Jin Wang, 2013. "Optimizing Scoring Function of Protein-Nucleic Acid Interactions with Both Affinity and Specificity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.
    3. Patrick Löffler & Samuel Schmitz & Enrico Hupfeld & Reinhard Sterner & Rainer Merkl, 2017. "Rosetta:MSF: a modular framework for multi-state computational protein design," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-24, June.
    4. Sarel J Fleishman & Andrew Leaver-Fay & Jacob E Corn & Eva-Maria Strauch & Sagar D Khare & Nobuyasu Koga & Justin Ashworth & Paul Murphy & Florian Richter & Gordon Lemmon & Jens Meiler & David Baker, 2011. "RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-10, June.
    5. Vanessa M. Macias & Johanna R. Ohm & Jason L. Rasgon, 2017. "Gene Drive for Mosquito Control: Where Did It Come from and Where Are We Headed?," IJERPH, MDPI, vol. 14(9), pages 1-30, September.
    6. Alexander M Sevy & Tim M Jacobs & James E Crowe Jr. & Jens Meiler, 2015. "Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-23, July.

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