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

Autor: Thean DGL; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China., Chu HY; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China.; Centre for Oncology and Immunology Limited, Hong Kong Science Park, Hong Kong, SAR, China., Fong JHC; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China., Chan BKC; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China.; Centre for Oncology and Immunology Limited, Hong Kong Science Park, Hong Kong, SAR, China., Zhou P; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China.; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China., Kwok CCS; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China., Chan YM; Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong, SAR, China., Mak SYL; Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong, SAR, China., Choi GCG; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China.; Centre for Oncology and Immunology Limited, Hong Kong Science Park, Hong Kong, SAR, China., Ho JWK; School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China.; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong, SAR, China., Zheng Z; Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong, SAR, China.; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, SAR, China.; Biotechnology and Health Centre, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China., Wong ASL; Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Hong Kong, SAR, China. aslw@hku.hk.; Centre for Oncology and Immunology Limited, Hong Kong Science Park, Hong Kong, SAR, China. aslw@hku.hk.; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, SAR, China. aslw@hku.hk.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Apr 25; Vol. 13 (1), pp. 2219. Date of Electronic Publication: 2022 Apr 25.
DOI: 10.1038/s41467-022-29874-5
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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