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Structure-guided discovery of highly efficient cytidine deaminases with sequence-context independence

文献类型： 外文期刊

作者： Xu, Kui 1 ; Feng, Hu 1 ; Zhang, Haihang 1 ; He, Chenfei 1 ; Kang, Huifang 1 ; Yuan, Tanglong 1 ; Shi, Lei 1 ; Zhou, Chikai 1 ; Hua, Guoying 1 ; Cao, Yaqi 1 ; Zuo, Zhenrui 1 ; Zuo, Erwei 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Lab Minist Agr & Rural Affairs,Key Laboratory of S, Shenzhen Branch,Guangdong Lab Lingnan Modern Agr,K, Shenzhen 518124, Peoples R China

期刊名称：NATURE BIOMEDICAL ENGINEERING （ 影响因子：28.1； 五年影响因子：29.3 ）

ISSN： 2157-846X

年卷期： 2024 年

页码：

收录情况： SCI

摘要： The applicability of cytosine base editors is hindered by their dependence on sequence context and by off-target effects. Here, by using AlphaFold2 to predict the three-dimensional structure of 1,483 cytidine deaminases and by experimentally characterizing representative deaminases (selected from each structural cluster after categorizing them via partitional clustering), we report the discovery of a few deaminases with high editing efficiencies, diverse editing windows and increased ratios of on-target to off-target effects. Specifically, several deaminases induced C-to-T conversions with comparable efficiency at AC/TC/CC/GC sites, the deaminases could introduce stop codons in single-copy and multi-copy genes in mammalian cells without double-strand breaks, and some residue conversions at predicted DNA-interacting sites reduced off-target effects. Structure-based generative machine learning could be further leveraged to expand the applicability of base editors in gene therapies. Cytidine deaminases with high editing efficiencies, diverse editing windows and increased ratios of on-target to off-target effects can be discovered via structure-based generative machine learning.

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