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Predicting the efficiency of prime editing guide RNAs in human cells

Authors
HuiKwon KimGOO SANG YUJINMAN PARKSeonwoo MinSungtae LeeSungroh YoonHYONGBUM HENRY KIM
Issue Date
Feb-2021
Publisher
NATURE PUBLISHING GROUP
Keywords
RNAs (pegRNAs); prime editing
Citation
NATURE BIOTECHNOLOGY, v.39, no.2, pp.198 - 206
Journal Title
NATURE BIOTECHNOLOGY
Volume
39
Number
2
Start Page
198
End Page
206
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5257
DOI
10.1038/s41587-020-0677-y
ISSN
1087-0156
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.
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