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The road to fully programmable protein catalysis

Author

Listed:
  • Sarah L. Lovelock

    (School of Chemistry, University of Manchester)

  • Rebecca Crawshaw

    (School of Chemistry, University of Manchester)

  • Sophie Basler

    (ETH Zürich)

  • Colin Levy

    (School of Chemistry, University of Manchester)

  • David Baker

    (University of Washington
    University of Washington
    University of Washington)

  • Donald Hilvert

    (ETH Zürich)

  • Anthony P. Green

    (School of Chemistry, University of Manchester)

Abstract

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.

Suggested Citation

  • Sarah L. Lovelock & Rebecca Crawshaw & Sophie Basler & Colin Levy & David Baker & Donald Hilvert & Anthony P. Green, 2022. "The road to fully programmable protein catalysis," Nature, Nature, vol. 606(7912), pages 49-58, June.
  • Handle: RePEc:nat:nature:v:606:y:2022:i:7912:d:10.1038_s41586-022-04456-z
    DOI: 10.1038/s41586-022-04456-z
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    Citations

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    Cited by:

    1. Xudong Wang & Chris Neale & Soo-Kyung Kim & William A. Goddard & Libin Ye, 2023. "Intermediate-state-trapped mutants pinpoint G protein-coupled receptor conformational allostery," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Amy E. Hutton & Jake Foster & Rebecca Crawshaw & Florence J. Hardy & Linus O. Johannissen & Thomas M. Lister & Emilie F. Gérard & Zachary Birch-Price & Richard Obexer & Sam Hay & Anthony P. Green, 2024. "A non-canonical nucleophile unlocks a new mechanistic pathway in a designed enzyme," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Peihua Lin & Bo Zhang & Hongli Yang & Shengfei Yang & Pengpeng Xue & Ying Chen & Shiyi Yu & Jichao Zhang & Yixiao Zhang & Liwei Chen & Chunhai Fan & Fangyuan Li & Daishun Ling, 2024. "An artificial protein modulator reprogramming neuronal protein functions," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Haoran Huang & Tao Yan & Chang Liu & Yuxiang Lu & Zhigang Wu & Xingchu Wang & Jie Wang, 2024. "Genetically encoded Nδ-vinyl histidine for the evolution of enzyme catalytic center," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Jun-Kuan Li & Ge Qu & Xu Li & Yuchen Tian & Chengsen Cui & Fa-Guang Zhang & Wuyuan Zhang & Jun-An Ma & Manfred T. Reetz & Zhoutong Sun, 2022. "Rational enzyme design for enabling biocatalytic Baldwin cyclization and asymmetric synthesis of chiral heterocycles," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Enrico Orsi & Lennart Schada von Borzyskowski & Stephan Noack & Pablo I. Nikel & Steffen N. Lindner, 2024. "Automated in vivo enzyme engineering accelerates biocatalyst optimization," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Thuy N. Nguyen & Christine Ingle & Samuel Thompson & Kimberly A. Reynolds, 2024. "The genetic landscape of a metabolic interaction," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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