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Abstract
Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman’s correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.
Prime editing is optimized by a method to choose the most efficient guide RNA.
Details
; Yu, Goosang 2
; Park, Jinman 2 ; Min, Seonwoo 3 ; Lee, Sungtae 4 ; Yoon, Sungroh 5
; Kim, Hyongbum Henry 6
1 Yonsei University College of Medicine, Department of Pharmacology, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Yonsei University College of Medicine, Brain Korea 21 Plus Project for Medical Sciences, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Institute for Basic Science (IBS), Center for Nanomedicine, Seoul, Republic of Korea (GRID:grid.410720.0) (ISNI:0000 0004 1784 4496); Yonsei University, Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
2 Yonsei University College of Medicine, Department of Pharmacology, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Yonsei University College of Medicine, Brain Korea 21 Plus Project for Medical Sciences, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
3 Seoul National University, Electrical and Computer Engineering, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
4 Yonsei University College of Medicine, Department of Pharmacology, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
5 Seoul National University, Electrical and Computer Engineering, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University, Interdisciplinary Program in Bioinformatics, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University, Graduate School of Data Science, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905)
6 Yonsei University College of Medicine, Department of Pharmacology, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Yonsei University College of Medicine, Brain Korea 21 Plus Project for Medical Sciences, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Institute for Basic Science (IBS), Center for Nanomedicine, Seoul, Republic of Korea (GRID:grid.410720.0) (ISNI:0000 0004 1784 4496); Yonsei University, Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Yonsei University College of Medicine, Severance Biomedical Science Institute, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454); Yonsei University, Graduate Program of NanoScience and Technology, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)