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Deep learning models to predict the editing efficiencies and outcomes of diverse base editors

Nature Biotechnology volume 42, pages 484–497 (2024)Cite this article

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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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Fig. 1: The editing window, product purity and preferred motifs for CBE and ABE variants.
Fig. 2: Editing window, product purity and preferred motifs for CGBE variants.
Fig. 3: Comparison of Cas9 variants with different PAM compatibilities and incorporation of the variants as the nickase domain for BEs.
Fig. 4: Specificity of SpCas9-YE1-BE4max, SpCas9-ABE8e(V106W) and SpCas9 variants with different PAM compatibilities.
Fig. 5: Deep-learning-based computational prediction of activities of Cas9 nuclease variants and BE variants.
Fig. 6: Correction of pathogenic or likely pathogenic single nucleotide polymorphisms using BEs.

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Data availability

We have submitted the deep sequencing data from this study to the NCBI Sequence Read Archive under accession number PRJNA821929 (ref. 52). We provide the datasets used in this study as Supplementary Tables 1–9. Source data are provided with this paper.

Code availability

We have made the source code for DeepBE available on Github at https://github.com/NahyeKim/DeepBE (ref. 53).

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Acknowledgements

We would like to thank Y. Kim and S. Park for assisting with the experiments and S. H. Kwon and J. Yu for providing technical advice. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2022R1A3B1078084 (H.H.K.), 2018R1A5A2025079 (H.H.K.) and 2022R1C1C2004229 (N.K.)), the Bio and Medical Technology Development Program of the NRF funded by the Korean government (MSIT) (2022M3A9E4017127 (H.H.K.) and 2022M3A9F3017506 (H.H.K.)), the Korea Drug Development Fund funded by the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, and the Ministry of Health and Welfare, Republic of Korea (HN21C0917 (H.H.K.)), the Yonsei Signature Research Cluster Program of 2021-22-0014 (H.H.K.), the Brain Korea 21 FOUR Project for Medical Science (Yonsei University College of Medicine), the SNUH Kun-hee Lee Child Cancer and Rare Disease Project, Republic of Korea (22B-000-0101 (H.H.K.)), the Yonsei Fellow Program, funded by Lee Youn Jae, and the Korea Health Technology R&D Project funded by the Ministry of Health and Welfare, Republic of Korea (HI21C1314 (H.H.K.)).

Author information

Author notes

  1. These authors contributed equally: Nahye Kim, Sungchul Choi.

Authors and Affiliations

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

    Nahye Kim, Sungchul Choi, Myungjae Song, Jinman Park & Hyongbum Henry Kim

  2. Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea

    Nahye Kim, Myungjae Song, Jinman Park, Sung-Rae Cho & Hyongbum Henry Kim

  3. Precision Medicine Institute, Macrogen, Seoul, Republic of Korea

    Sungjae Kim

  4. Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jung Hwa Seo & Sung-Rae Cho

  5. LG AI Research, Seoul, Republic of Korea

    Seonwoo Min

  6. Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea

    Sung-Rae Cho

  7. Rehabilitation Institute of Neuromuscular Disease, Yonsei University College of Medicine, Seoul, Republic of Korea

    Sung-Rae Cho

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

    Hyongbum Henry Kim

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

    Hyongbum Henry Kim

  10. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hyongbum Henry Kim

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

    Hyongbum Henry Kim

  12. Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  13. Department of Otolaryngology, University of California, San Francisco, CA, USA

    Hyongbum Henry Kim

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Contributions

N.K. performed all wet experiments, including the high-throughput evaluations. S.C. and N.K. developed deep-learning-based prediction models. M.S. and N.K. determined base editing efficiencies at endogenous sites. S.M. developed related web tools. S.K. contributed substantially to bioinformatics analyses. J.P. contributed to bioinformatic analyses. J.H.S. and S.-R.C. performed western blotting to measure Cas9 variant and BE variant protein levels. H.K.K. and N.K. conceived and designed the study. N.K. and H.H.K. analyzed the data and wrote the paper.

Corresponding author

Correspondence to Hyongbum Henry Kim.

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Competing interests

Yonsei University has filed a patent (10-2022-0053742) based on this work, in which N.K. and H.H.K. are listed as the coinventors. H.H.K. is a consultant for EcoR1 capital.

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Nature Biotechnology thanks Hui Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Generation and evaluation of variant-expressing cell lines.

a, Schematic overview of the library experiment. b, Western blot analysis of Cas9 protein levels in cells expressing Cas9 variants, base editor variants with different deaminases, and base editor variants containing Cas9 variants. The results from three independent western blot analyses are presented (n = 3). Error bars show mean ± s.e.m. Subsets of variants without statistically significant differences (P > 0.05, analysis of variance (ANOVA) followed by Tukey’s post hoc test (two-sided)) in their protein levels are indicated by letters, such as a and b.

Source data

Extended Data Fig. 2 Comparison of base editing efficiencies induced by different CBEs (a) and ABEs (b).

The red triangles indicate target sequences at which the editing efficiency of one base editor is at least 30% higher than that of the other base editor. Heatmaps show the relative distribution of nucleotides neighboring the target nucleotide in the target sequences within the red triangles. Results at position 6 are also shown in Fig. 1d, h. The number of target sequences (n) = 1,027 and 2,903 at position 5, 917 and 3,119 for position 6, and 701 and 3,255 at position 7 for NG-YE1-BE4max and NG-SsAPOBEC3B, respectively (a) and 4,026 and 701 at position 4, 3,448 and 698 at position 5, 3,258 and 674 at position 6, 3,254 and 847 at position 7, and 3,255 and 1,081 at position 8 for NG-ABE8e(V106W) and NG-ABE8.17-m + V106W, respectively (b).

Extended Data Fig. 3 Comparison of base editing efficiencies induced by different CGBEs.

The red triangles indicate target sequences at which the editing efficiency of one base editor is at least 30% higher than that of the other base editor. Heatmaps show the relative distribution of nucleotides neighboring the target nucleotide in target sequences within the red triangles. Results at position 6 are also shown in Fig. 2d–f. The number of target sequences (n) = 1,807 and 830 at position 5, 1,413 and 957 at position 6, and 1,249 and 1,227 at position 7 for NG-CGBE1 and NG-miniCGBE1, respectively (a), 2,643 and 972 at position 5, 2,894 and 817 at position 6, 2,706 and 838 at position 7 for NG-miniCGBE1 and NG-APOBEC-nCas9-Ung, respectively (b), and 2,885 and 808 at position 5, 2,995 and 766 at position 6, and 2,715 and 891 at position 7 for NG-CGBE1 and NG-APOBEC-nCas9-Ung, respectively (c).

Extended Data Fig. 4 Average Cas9 variant-induced indel frequencies at target sequences containing each 4-nt PAM sequence.

If the average indel frequencies were lower than 10%, the corresponding candidate PAMs are shown in white. The red boxes indicate PAM sequences that were associated with the highest average indel frequencies among the examined Cas9 variants with different PAM compatibilities; these PAM sequences at which the corresponding Cas9 variants showed the highest average indel frequencies are also shown in Fig. 3b. SpCas9 (a), VRAR variant (b), xCas9 (c), SpCas9-NG (d), SpCas9-NRRH (e), SpCas9-NRRH (f), SpCas9-NRCH (g), SpG (h), SpRY (l), and Sc++ (j) are used.

Extended Data Fig. 5 Comparison of Cas9 variants with different PAM compatibilities.

Maximum average indel frequencies generated by any of the ten Cas9 variants (left heatmaps) and the corresponding Cas9 variants that showed the highest average activities (right heatmaps) at target sequences containing each 4-nt PAM sequence. If the maximum average indel frequencies were lower than 5% (a) and 20% (b), the corresponding candidate PAMs were shown in white.

Extended Data Fig. 6 Representative dot plots showing correlations between indel frequencies induced by Cas9 variants at targets with 12 example PAMs.

The Pearson correlation coefficients (r) are shown. The number of target sequences (n) = 29 for SpCas9 and VRQR, 30 for SpCas9 and xCas9, 30 for SpCas9 and SpCas9-NG, 30 for SpCas9 and SpCas9-NRRH, 28 for SpCas9 and SpCas9-NRTH, 30 for SpCas9 and SpCas9-NRCH, 30 for SpCas9 and SpRY, 30 for VRQR and SpCas9-NRRH, 30 for xCas9 and SpCas9-NRRH, 29 for SpCas9-NRCH and SpG, and 28 for SpCas9-NRCH and SpRY.

Extended Data Fig. 7 Correlations between predicted DeepNG-BE_efficiency and DeepNG-BE_proportion_30bp scores and measured base editing efficiencies (a) and proportions (b).

The Pearson correlation coefficient (r) and the Spearman correlation (R) are shown. The number of target sequences (n) = 1,753, 1,998, 2,218, 2,214, 1,754, 1,752, and 1,750 (a) and 2,633, 6,446, 3,212, 2,320, 1,060, 859, and 748 (b) for NG-YE1-BE4max, NG-SsAPOBEC3B, NG-ABE8e(V106W), NG-ABE8.17-m + V106W, NG-CGBE1, NG-miniCGBE1, and NG-APOBEC-nCas9-Ung, respectively.

Extended Data Fig. 8 Architecture of DeepBE generation.

To predict overall base editing efficiency (DeepBE-efficiency), we used the prediction scores of DeepCas9variants along with input sequence information for the base editing window ± 1 base pair. Additionally, we obtained DeepBE-proportion scores based on the 20-base pair target sequence information. The DeepBE-proportion scores and DeepBE-efficiency scores were multiplied together to generate DeepBE prediction scores.

Extended Data Fig. 9 Performance of conventional machine learning- or shallow learning-based models and deep-learning-based models in predicting the efficiencies of Cas9 nuclease (a) and base editor variants (b).

Heatmaps show Pearson’s (left panel) and Spearman’s (right panel) correlation coefficients. In b, the deamination and nuclease domains are shown to indicate base editor variants.

Extended Data Fig. 10 Evaluation of DeepBE in predicting base editor activities at endogenous targets in three different cell lines.

a-c, Correlations between DeepBE-predicted base editing outcome frequencies and measured outcome frequencies at endogenous targets in HEK293T (a), HCT116 (b), and U20S (c) cells. The number of targets (n) = 240, 312, and 174 (a), 219, 293, and 158 (b), and 219, 330, and 181 (c) for SpCas9-NRCH-YE1-BE4max, SpCas9-NRCH-ABE8e(V106W), and SpCas9-NRCH-CGBE1, respectively. d,e, Comparison of measured base editing outcome frequencies at targets with different chromatin accessibilities (d) and different functional regions (that is, coding versus non-coding) (e). The boxes represent the 25th, 50th, and 75th percentiles; the whiskers show the 10th and 90th percentiles. The Wilcoxon rank-sum test; two-sided. The number of targets (n) = 114 (Non-DNase I hypersensitive sites (DHS)) and 33 (DHS; HEK293T), 102 and 40 (HCT116), and 98 and 28 (U2OS), 267 and 44 (HEK293T), 246 and 47 (HCT116), and 269 and 61 (U2OS), and 144 and 30 (HEK293T), 71 and 23 (HCT116), and 151 (Non-DHS) and 30 (HDS; U2OS) (d) and 79 (coding) and 68 (non-coding; HEK293T), 80 and 62 (HCT116), and 71 and 55 (U2OS), 150 and 161 (HEK293T), 149 and 144 (HCT116), and 143 and 187 (U2OS), and 78 and 96 (HEK293T), 79 and 40 (HCT116), and 89 (coding) and 92 (non-coding; U2OS) (e) for NRCH-YE1-BE4max, NRCH-ABE8e(V106W), and NRCH-CGBE1, respectively.

Supplementary information

Supplementary Information

Supplementary Figs. 1–24.

Reporting Summary

Supplementary Tables 1–9

Supplementary Tables 1–9 (titles and descriptions of each table are included in each spreadsheet file).

Source data

Source Data Extended Data Fig. 1

Unprocessed original images of gels and western blots.

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Kim, N., Choi, S., Kim, S. et al. Deep learning models to predict the editing efficiencies and outcomes of diverse base editors. Nat Biotechnol 42, 484–497 (2024). https://doi.org/10.1038/s41587-023-01792-x

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