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Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities

Author

Listed:
  • Dawn G. L. Thean

    (The University of Hong Kong)

  • Hoi Yee Chu

    (The University of Hong Kong
    Hong Kong Science Park)

  • John H. C. Fong

    (The University of Hong Kong)

  • Becky K. C. Chan

    (The University of Hong Kong
    Hong Kong Science Park)

  • Peng Zhou

    (The University of Hong Kong
    The University of Hong Kong)

  • Cynthia C. S. Kwok

    (The University of Hong Kong)

  • Yee Man Chan

    (Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet)

  • Silvia Y. L. Mak

    (Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet)

  • Gigi C. G. Choi

    (The University of Hong Kong
    Hong Kong Science Park)

  • Joshua W. K. Ho

    (The University of Hong Kong
    Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park)

  • Zongli Zheng

    (Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet
    City University of Hong Kong
    City University of Hong Kong Shenzhen Research Institute)

  • Alan S. L. Wong

    (The University of Hong Kong
    Hong Kong Science Park
    The University of Hong Kong)

Abstract

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9’s activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor’s activity.

Suggested Citation

  • Dawn G. L. Thean & Hoi Yee Chu & John H. C. Fong & Becky K. C. Chan & Peng Zhou & Cynthia C. S. Kwok & Yee Man Chan & Silvia Y. L. Mak & Gigi C. G. Choi & Joshua W. K. Ho & Zongli Zheng & Alan S. L. W, 2022. "Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29874-5
    DOI: 10.1038/s41467-022-29874-5
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    References listed on IDEAS

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