A research team from the Faculty of Medicine of the University of Hong Kong has combined artificial intelligence and protein engineering technology to discover more effective CRISPR-Cas9 variants that can be applied in gene therapy.
The research expands larger virtual data for analysis, so that variant data can be greatly increased by 20 times, and the speed of screening can be accelerated.
The research team applied the method to improve multiple Cas9 proteins and designed a variant of Staphylococcus aureus Cas9 (SaCas9) with enhanced gene editing efficiency.
Members of the research team include (from right): Assistant Professor Dr. Huang Zhaolin, Department of Biomedical Sciences, HKU Faculty of Medicine, Assistant Researcher Miss Dawn Thean Gek-lian and Postdoctoral Researcher Dr. Chu Kaiyi.
(Provided by HKU)
The research team, led by Assistant Professor Huang Zhaolin, Department of Biomedical Sciences, Faculty of Medicine, HKU, integrated "machine learning" into a high-throughput screening platform, combining multiple point mutations in the PI and WED domains to design a more active SaCas9 protein.
PAM is very important for the editable target of Cas9. By weakening the interaction between PAM and DNA, thereby reducing the editing restriction brought by PAM, Cas9 can edit a wider range of gene targets, in order to make up for the weakened interaction with DNA. The interaction needs to strengthen the interaction with DNA at the same time as the WED domain to enhance the editing ability of Cas9.
Protein modeling analysis also predicts new improved variants
During the screening and subsequent validation, the team identified new variants, including one called KKH-SaCas9-plus, which had enhanced activity at specific genomic loci by up to 33%.
The protein modeling analysis also predicted new and improved variants with the opportunity to add new interactions between the WED and PI domains and DNA duplexes with PAM.
Structure-directed design has dominated the field of Cas9 improvement engineering, however, it has only explored a few sites, amino acid residue mutations, and combinatorial mutations at multiple sites.
The study found that the combination of "machine learning" and multi-point combinatorial mutation screening research can help maximize the output of experimental data, greatly reduce the screening time and cost of experiments, and find more efficient variants from more variants. Variant KKH-SaCas9-plus.
A patent application has been filed for the research results
Huang Zhaolin said that this method will greatly speed up the improvement of Cas9 protein, so that genome editing technology can be more effectively applied to treat genetic diseases.
Relevant research results have been published in the international scientific journal "Nature-Communications", and a patent application has been submitted for this.
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