한빛사논문
Nahye Kim 1,2,14, Sungchul Choi 1,14, Sungjae Kim 3, Myungjae Song 1,2, Jung Hwa Seo 4, Seonwoo Min 5, Jinman Park 1,2, Sung-Rae Cho 2,4,6,7 & Hyongbum Henry Kim 1,2,8,9,10,11,12,13
1Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.
2Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea.
3Precision Medicine Institute, Macrogen, Seoul, Republic of Korea.
4Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
5LG AI Research, Seoul, Republic of Korea.
6Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.
7Rehabilitation Institute of Neuromuscular Disease, Yonsei University College of Medicine, Seoul, Republic of Korea.
8Graduate Program of NanoScience and Technology, Yonsei University, Seoul, Republic of Korea.
9Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. 10Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.
11Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
12Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea.
13Department of Otolaryngology, University of California, San Francisco, CA, USA.
14These authors contributed equally: Nahye Kim, Sungchul Choi.
Correspondence to Hyongbum Henry Kim.
Abstract
Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three C•G to G•C BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.
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