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Predicting the efficiency of prime editing guide RNAs in human cells

Nature Biotechnology volume 39, pages 198–206 (2021)Cite this article

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An Author Correction to this article was published on 08 February 2024

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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.

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Fig. 1: High-throughput evaluation of PE2 activity using libraries of pegRNA–target sequence pairs.
Fig. 2: Factors affecting PE2 efficiency.
Fig. 3: Effects of editing type and position on PE2 efficiency.
Fig. 4: Development of computational models for predicting PE2 efficiencies.

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Data availability

The deep sequencing data from this study have been submitted to the National Center for Biotechnology Information Sequence Read Archive under accession number PRJNA624815. The data sets used in this study are provided as Supplementary Tables 3, 4 and 5.

Code availability

Source codes for DeepPE and the custom Python script used for the prime editing efficiency calculations are provided as Supplementary Codes 1 and 2 and are also available at https://github.com/hkimlab-PE/PE_SupplementaryCode.

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Acknowledgements

We would like to thank D. Kim, S. Park and Y. Kim for assisting with the experiments. This work was supported, in part, by the National Research Foundation of Korea (grants 2017R1A2B3004198 (H.H.K.), 2017M3A9B4062403 (H.H.K.), 2020R1C1C1003284 (H.K.K) and 2018R1A5A2025079 (H.H.K)), the Brain Korea 21 Plus Project (Yonsei University College of Medicine) and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (grants HI17C0676 (H.H.K.) and HI16C1012 (H.H.K.)).

Author information

Author notes

  1. These authors contributed equally: Hui Kwon Kim, Goosang Yu.

Authors and Affiliations

  1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hui Kwon Kim, Goosang Yu, Jinman Park, Sungtae Lee & Hyongbum Henry Kim

  2. Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hui Kwon Kim, Goosang Yu, Jinman Park & Hyongbum Henry Kim

  3. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hui Kwon Kim & Hyongbum Henry Kim

  4. Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea

    Hui Kwon Kim & Hyongbum Henry Kim

  5. Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea

    Seonwoo Min & Sungroh Yoon

  6. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea

    Sungroh Yoon

  7. Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea

    Sungroh Yoon

  8. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  9. Graduate Program of NanoScience and Technology, Yonsei University, Seoul, Republic of Korea

    Hyongbum Henry Kim

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Contributions

G.Y. and H.K.K. performed the wet experiments, including high-throughput evaluation of PE2 efficiencies. S.M., S.L., S.Y. and H.K.K. developed DeepPE and the related web tools. J.P. substantially contributed to bioinformatics analyses and DeepPE development. H.K.K. and H.H.K. conceived of and designed the study. H.K.K., G.Y. and H.H.K. analyzed the data and wrote the manuscript.

Corresponding author

Correspondence to Hyongbum Henry Kim.

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Competing interests

Yonsei University has filed a patent application based on this work, in which H.K.K., G.Y. and H.H.K. are listed as inventors.

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Supplementary information

Supplementary Information

Supplementary Texts 1–3, Supplementary Figs. 1–23 and Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Table 3

Data sets of PE2 efficiencies at endogenous sites

Supplementary Table 4

Data sets HT-training, HT-test, Type-training, Type-test, Position-training and Position-test

Supplementary Table 5

Data sets of PE2 efficiencies generated using HCT116 and MDA-MB-231 cells

Supplementary Table 6

Oligonucleotides used in this study

Supplementary Table 7

Exact P values for Figs. 2 and 3 and Supplementary Fig. 15

Supplementary Software 1

Codes relevant to the PE efficiency analysis

Supplementary Software 2

Codes relevant to DeepPE

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Kim, H.K., Yu, G., Park, J. et al. Predicting the efficiency of prime editing guide RNAs in human cells. Nat Biotechnol 39, 198–206 (2021). https://doi.org/10.1038/s41587-020-0677-y

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