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Predicting the efficiency of prime editing guide RNAs in human cells

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Title
Predicting the efficiency of prime editing guide RNAs in human cells
Author(s)
Hui Kwon Kim; Goosang Yu; Jinman Park; Seonwoo Min; Sungtae Lee; Sungroh Yoon; Hyongbum Henry Kim
Publication Date
2021-02
Journal
NATURE BIOTECHNOLOGY, v.39, no.2, pp.198 - 206
Publisher
NATURE RESEARCH
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.
URI
https://pr.ibs.re.kr/handle/8788114/9964
DOI
10.1038/s41587-020-0677-y
ISSN
1087-0156
Appears in Collections:
Center for Nanomedicine (나노의학 연구단) > 1. Journal Papers (저널논문)
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