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

Research output: Contribution to journalArticlepeer-review

111 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 languageEnglish
Pages (from-to)1328-1336
Number of pages9
JournalNature Biotechnology
Volume38
Issue number11
DOIs
Publication statusPublished - 2020 Nov 1

Bibliographical note

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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