Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning

Luke W. Koblan, Mandana Arbab, Max W. 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

Research output: Contribution to journalArticlepeer-review

122 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1414-1425
Number of pages12
JournalNature biotechnology
Issue number11
StatePublished - Nov 2021

All Science Journal Classification (ASJC) codes

  • Applied Microbiology and Biotechnology
  • Bioengineering
  • Molecular Medicine
  • Biotechnology
  • Biomedical Engineering


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