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

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 and Alan S. L. Wong ()
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Dawn G. L. Thean: The University of Hong Kong
Hoi Yee Chu: The University of Hong Kong
John H. C. Fong: The University of Hong Kong
Becky K. C. Chan: The University of Hong Kong
Peng Zhou: 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
Joshua W. K. Ho: The University of Hong Kong
Zongli Zheng: Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet
Alan S. L. Wong: The University of Hong Kong

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29874-5

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