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.

胞嘧啶碱基编辑器的适用性因其对序列背景的依赖和脱靶效应而受到阻碍。在这里，通过使用 AlphaFold2 预测 1,483 个胞苷脱氨酶的三维结构，并通过实验表征代表性脱氨酶（通过分区聚类对每个结构簇进行分类后选出），我们报告发现了一些具有高编辑效率、多样化的脱氨酶。编辑窗口并增加了目标与脱靶效果的比率。具体来说，几种脱氨酶在 AC/TC/CC/GC 位点以相当的效率诱导 C 到 T 的转化，脱氨酶可以在哺乳动物细胞的单拷贝和多拷贝基因中引入终止密码子，而不会出现双链断裂，并且一些预测的 DNA 相互作用位点的残基转化减少了脱靶效应。可以进一步利用基于结构的生成机器学习来扩展碱基编辑器在基因治疗中的适用性。