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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
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    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. 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.
    3. 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.
    4. 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).
    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.

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