Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

文献类型： 外文期刊

第一作者： Yuan, Tanglong

作者： Yuan, Tanglong;Yan, Nana;Zheng, Jitan;Li, Nana;Liu, Jing;Zhang, Haihang;Xie, Long;Li, Di;Shi, Lei;Sun, Yongsen;Li, Yongyao;Zuo, Erwei;Fei, Tianyi;Meng, Juan;Ying, Wenqin;Sun, Yidi;Zheng, Jitan;Zheng, Jitan;Li, Di;Li, Yixue

作者机构：

期刊名称：NATURE COMMUNICATIONS （ 影响因子：14.919； 五年影响因子：15.805 ）

ISSN：

年卷期： 2021 年 12 卷 1 期

页码：

收录情况： SCI

摘要： C->G transversions can be highly desirable editing outcomes. Here the authors optimise CGBEs and provide a deep learning model for predicting editing outcomes based on sequence context. Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序