横截
计算机科学
人工智能
编码(社会科学)
基础(拓扑)
清脆的
机器学习
计算生物学
生物
基因
遗传学
数学
统计
数学分析
突变
作者
Luke W. Koblan,Mandana Arbab,Max Shen,Jeffrey A. Hussmann,Andrew V. Anzalone,Jordan L. Doman,Gregory A. Newby,Dian Yang,Beverly Mok,Joseph M. Replogle,Albert Xu,Tyler A. Sisley,Jonathan S. Weissman,Britt Adamson,David R. Liu
标识
DOI:10.1038/s41587-021-00938-z
摘要
Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes (R = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE-single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant.
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