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

IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Kui Xu, Hu Feng, Haihang Zhang, Chenfei He, Huifang Kang, Tanglong Yuan, Lei Shi, Chikai Zhou, Guoying Hua, Yaqi Cao, Zhenrui Zuo, Erwei Zuo
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Abstract

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.

Abstract Image

在结构引导下发现与序列上下文无关的高效胞苷脱氨酶
胞嘧啶碱基编辑器的适用性因其对序列上下文的依赖性和脱靶效应而受到阻碍。在这里,我们利用 AlphaFold2 预测了 1,483 个胞苷脱氨酶的三维结构,并通过实验鉴定了具有代表性的脱氨酶(通过分区聚类从每个结构簇中选出),报告了我们发现的一些具有高编辑效率、多种编辑窗口以及更高的靶上效应与脱靶效应比率的脱氨酶。具体来说,几种脱氨酶在AC/TC/CC/GC位点诱导C-T转换的效率相当,这些脱氨酶可以在哺乳动物细胞中的单拷贝和多拷贝基因中引入终止密码子而不会发生双链断裂,在预测的DNA相互作用位点的一些残基转换减少了脱靶效应。基于结构的生成机器学习可进一步扩大碱基编辑器在基因疗法中的应用。
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来源期刊
Nature Biomedical Engineering
Nature Biomedical Engineering Medicine-Medicine (miscellaneous)
CiteScore
45.30
自引率
1.10%
发文量
138
期刊介绍: Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.
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