IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v610y2022i7931d10.1038_s41586-022-05278-9.html
   My bibliography  Save this article

NMR-guided directed evolution

Author

Listed:
  • Sagar Bhattacharya

    (Syracuse University)

  • Eleonora G. Margheritis

    (Yokohama City University)

  • Katsuya Takahashi

    (Yokohama City University)

  • Alona Kulesha

    (Syracuse University)

  • Areetha D’Souza

    (Syracuse University)

  • Inhye Kim

    (Syracuse University)

  • Jennifer H. Yoon

    (Syracuse University)

  • Jeremy R. H. Tame

    (Yokohama City University)

  • Alexander N. Volkov

    (Vlaams Instituut voor Biotechnologie (VIB)
    Vrije Universiteit Brussel (VUB))

  • Olga V. Makhlynets

    (Syracuse University)

  • Ivan V. Korendovych

    (Syracuse University)

Abstract

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1–4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6–9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Suggested Citation

  • Sagar Bhattacharya & Eleonora G. Margheritis & Katsuya Takahashi & Alona Kulesha & Areetha D’Souza & Inhye Kim & Jennifer H. Yoon & Jeremy R. H. Tame & Alexander N. Volkov & Olga V. Makhlynets & Ivan , 2022. "NMR-guided directed evolution," Nature, Nature, vol. 610(7931), pages 389-393, October.
  • Handle: RePEc:nat:nature:v:610:y:2022:i:7931:d:10.1038_s41586-022-05278-9
    DOI: 10.1038/s41586-022-05278-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-022-05278-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-022-05278-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Zhenye & Lin, Yongjia & Li, You, 2023. "Does corporate engagement in digital transformation influence greenwashing? Evidence from China," Finance Research Letters, Elsevier, vol. 58(PD).
    2. Picatoste, Aitor & Justel, Daniel & Mendoza, Joan Manuel F., 2022. "Circularity and life cycle environmental impact assessment of batteries for electric vehicles: Industrial challenges, best practices and research guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    3. Elisabetta Allevi & Maria Elena Giuli & Ruth Domínguez & Giorgia Oggioni, 2023. "Evaluating the role of waste-to-energy and cogeneration units in district heatings and electricity markets," Computational Management Science, Springer, vol. 20(1), pages 1-49, December.
    4. Christian Rusche & Jeanne Mouton, 2024. "The anti-steering provision of Article 5 (4) of the DMA: a law and economics assessment on the business model of gatekeepers and business users," European Journal of Law and Economics, Springer, vol. 57(1), pages 207-237, April.
    5. Bob Schiffrin & Joel A. Crossley & Martin Walko & Jonathan M. Machin & G. Nasir Khan & Iain W. Manfield & Andrew J. Wilson & David J. Brockwell & Tomas Fessl & Antonio N. Calabrese & Sheena E. Radford, 2024. "Dual client binding sites in the ATP-independent chaperone SurA," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:610:y:2022:i:7931:d:10.1038_s41586-022-05278-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.