Prediction of the sequence-specific cleavage activity of Cas9 variants

Nahye Kim; Hui Kwon Kim; Sungtae Lee; Jung Hwa Seo; Jae Woo Choi; Jinman Park; Seonwoo Min; Sungroh Yoon; Sung Rae Cho; Hyongbum Henry Kim

doi:10.1038/s41587-020-0537-9

Prediction of the sequence-specific cleavage activity of Cas9 variants

Nahye Kim, Hui Kwon Kim, Sungtae Lee, Jung Hwa Seo, Jae Woo Choi, Jinman Park, Seonwoo Min, Sungroh Yoon, Sung Rae Cho, Hyongbum Henry Kim

Department of Rehabilitation Medicine

Research output: Contribution to journal › Article › peer-review

71 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1328-1336
Number of pages	9
Journal	Nature Biotechnology
Volume	38
Issue number	11
DOIs	https://doi.org/10.1038/s41587-020-0537-9
Publication status	Published - 2020 Nov 1

Bibliographical note

Funding Information:
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.)).

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

All Science Journal Classification (ASJC) codes

Biotechnology
Bioengineering
Biomedical Engineering
Applied Microbiology and Biotechnology
Molecular Medicine

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10.1038/s41587-020-0537-9

Cite this

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title = "Prediction of the sequence-specific cleavage activity of Cas9 variants",

abstract = "Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme{\textquoteright}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.",

author = "Nahye Kim and Kim, {Hui Kwon} and Sungtae Lee and Seo, {Jung Hwa} and Choi, {Jae Woo} and Jinman Park and Seonwoo Min and Sungroh Yoon and Cho, {Sung Rae} and Kim, {Hyongbum Henry}",

note = "Funding Information: 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.)). Publisher Copyright: {\textcopyright} 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.",

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AU - Park, Jinman

AU - Min, Seonwoo

AU - Yoon, Sungroh

AU - Cho, Sung Rae

AU - Kim, Hyongbum Henry

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N2 - 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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Prediction of the sequence-specific cleavage activity of Cas9 variants

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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