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Prediction of the sequence-specific cleavage activity of Cas9 variants

Nature Biotechnology volume 38, pages 1328–1336 (2020)Cite this article

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Abstract

Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme’s specificity or to alter or broaden its protospacer-adjacent motif (PAM) compatibility, but selecting the optimal variant for a given target sequence and application remains difficult. To build computational models to predict the sequence-specific activity of 13 SpCas9 variants, we first assessed their cleavage efficiency at 26,891 target sequences. We found that, of the 256 possible four-nucleotide NNNN sequences, 156 can be used as a PAM by at least one of the SpCas9 variants. For the high-fidelity variants, overall activity could be ranked as SpCas9 ≥ Sniper-Cas9 > eSpCas9(1.1) > SpCas9-HF1 > HypaCas9 ≈ xCas9 >> evoCas9, whereas their overall specificities could be ranked as evoCas9 >> HypaCas9 ≥ SpCas9-HF1 ≈ eSpCas9(1.1) > xCas9 > Sniper-Cas9 > SpCas9. Using these data, we developed 16 deep-learning-based computational models that accurately predict the activity of these variants at any target sequence.

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Fig. 1: High-throughput evaluation of the activities of SpCas9 variants using a lentiviral library of sgRNA-target sequence pairs.
Fig. 2: PAM compatibilities and general activities of SpCas9 variants.
Fig. 3: Specificity of SpCas9 variants when there are mismatches between the sgRNA guide sequence and the target sequence.
Fig. 4: Development and evaluation of DeepSpCas9variants, computational models predicting the activities of SpCas9 variants.

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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 SRR10215483. We have provided the data sets used in this study as Supplementary Tables 1–3.

Code availability

We have made the source code for DeepSpCas9variants and the custom Python scripts used for the indel frequency calculations available on Github at https://github.com/NahyeKim/DeepSpCas9variants and https://github.com/CRISPRJWCHOI/CRISPR_toolkit/tree/master/Indel_searcher_2.

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Acknowledgements

We would like to thank Younggwang Kim, S. Park and Younghye Kim for assisting with the experiments. This work was supported in part by the National Research Foundation of Korea (grants 2017R1A2B3004198 (to H.H.K.), 2017M3A9B4062403 (to H.H.K.) and 2018R1A5A2025079 (to H.H.K.)), Brain Korea 21 Plus Project (Yonsei University College of Medicine), the Institute for Basic Science (IBS-R026-D1) and the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (grants HI17C0676 (to H.H.K.) and HI16C1012 (to S.-R.C. and H.H.K.)).

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Author notes

  1. These authors contributed equally: Nahye Kim, Hui Kwon Kim.

Authors and Affiliations

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

    Nahye Kim, Hui Kwon Kim, Sungtae Lee, Jae Woo Choi, Jinman Park & Hyongbum Henry Kim

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

    Nahye Kim, Hui Kwon Kim, Jung Hwa Seo, Jinman Park, Sung-Rae Cho & 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. Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jung Hwa Seo & Sung-Rae Cho

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

    Seonwoo Min & Sungroh Yoon

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

    Sungroh Yoon

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

    Sung-Rae Cho & Hyongbum Henry Kim

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

    Hyongbum Henry Kim

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Contributions

N.K. performed most wet experiments, including high-throughput evaluation of SpCas9 variant activities. H.K.K. helped substantially with N.K.’s experiments and provided critical technical advice for high-throughput experiments. S.L., S.M., S.Y. and H.K.K. developed DeepSpCas9variants and the related web tools. J.P. and J.W.C. contributed substantially to bioinformatics analyses. J.H.S. and S.-R.C. performed western blotting to measure SpCas9 variant protein levels. Together with H.K.K. and N.K., H.H.K. conceived and designed the study. N.K., H.K.K. 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 based on this work, in which N.K., H.K.K. and H.H.K. are the co-inventors (patent no. 10-2019-0127304).

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Supplementary information

Supplementary Information

Supplementary Text, Supplementary Discussions 1 and 2, Supplementary Figs. 1–17 and Supplementary Notes 1–3.

Reporting Summary

Supplementary Table 1

Library A design and indel frequencies from the library.

Supplementary Table 2

Library B design and indel frequencies from the library.

Supplementary Table 3

Library C design and indel frequencies from the library.

Supplementary Table 4

Frequency of shuffling between sgRNA-encoding and barcode-target sequences.

Supplementary Table 5

Average indel frequencies associated with all possible 4-nt PAM sequences.

Supplementary Table 6

PAM compatibilities determined using the 30 fixed protospacers from library A and a wide range of protospacers from library B.

Supplementary Table 7

Indel frequencies at 30 perfectly matched target sequences used for analyzing specificity. Of 30 guide RNAs, 8 were selected.

Supplementary Table 8

Data sets used for the development and evaluation of DeepSpCas9variants.

Supplementary Table 9

Primer sequences used for experiments.

Source data

Source Data Fig. 1

Unprocessed western blots for Fig. 1b

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Kim, N., Kim, H.K., Lee, S. et al. Prediction of the sequence-specific cleavage activity of Cas9 variants. Nat Biotechnol 38, 1328–1336 (2020). https://doi.org/10.1038/s41587-020-0537-9

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