Prediction of the sequence-specific cleavage activity of Cas9 variants
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 computatio...
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| Veröffentlicht in: | Nature biotechnology 2020-11, Vol.38 (11), p.1328-1336 |
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| creator | Kim, Nahye Kim, Hui Kwon Lee, Sungtae Seo, Jung Hwa Choi, Jae Woo Park, Jinman Min, Seonwoo Yoon, Sungroh Cho, Sung-Rae Kim, Hyongbum Henry |
| description | 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.
Deep-learning models predict the Cas9 variant with optimal activity and specificity for any target sequence. |
| doi_str_mv | 10.1038/s41587-020-0537-9 |
| format | Article |
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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.
Deep-learning models predict the Cas9 variant with optimal activity and specificity for any target sequence.</description><identifier>ISSN: 1087-0156</identifier><identifier>EISSN: 1546-1696</identifier><identifier>DOI: 10.1038/s41587-020-0537-9</identifier><identifier>PMID: 32514125</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>631/1647/1511 ; 631/1647/1513/1967/3196 ; Agriculture ; Base Sequence ; Bioinformatics ; Biomedical and Life Sciences ; Biomedical Engineering/Biotechnology ; Biomedicine ; Biotechnology ; Cleavage ; Computer applications ; CRISPR-Associated Protein 9 - genetics ; Deep Learning ; Forecasts and trends ; Gene Library ; Genomes ; HEK293 Cells ; Humans ; Identification and classification ; INDEL Mutation - genetics ; Intraspecific genetic variation ; Learning ; Lentivirus - genetics ; Life Sciences ; Machine learning ; Mathematical models ; Medical schools ; Medicine ; Models, Genetic ; Mutation - genetics ; Nucleotides ; Streptococcus pyogenes ; Technology application ; Transcription factors ; Transfer RNA ; University colleges ; Varieties</subject><ispartof>Nature biotechnology, 2020-11, Vol.38 (11), p.1328-1336</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2020</rights><rights>COPYRIGHT 2020 Nature Publishing Group</rights><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c610t-7a067e434888f41acd39ef470b9b7d047bd1d3f7c9433495e9c949e9649981923</citedby><cites>FETCH-LOGICAL-c610t-7a067e434888f41acd39ef470b9b7d047bd1d3f7c9433495e9c949e9649981923</cites><orcidid>0000-0003-0694-9244 ; 0000-0002-4693-738X ; 0000-0002-2367-197X ; 0000-0002-8489-7972</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32514125$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Nahye</creatorcontrib><creatorcontrib>Kim, Hui Kwon</creatorcontrib><creatorcontrib>Lee, Sungtae</creatorcontrib><creatorcontrib>Seo, Jung Hwa</creatorcontrib><creatorcontrib>Choi, Jae Woo</creatorcontrib><creatorcontrib>Park, Jinman</creatorcontrib><creatorcontrib>Min, Seonwoo</creatorcontrib><creatorcontrib>Yoon, Sungroh</creatorcontrib><creatorcontrib>Cho, Sung-Rae</creatorcontrib><creatorcontrib>Kim, Hyongbum Henry</creatorcontrib><title>Prediction of the sequence-specific cleavage activity of Cas9 variants</title><title>Nature biotechnology</title><addtitle>Nat Biotechnol</addtitle><addtitle>Nat Biotechnol</addtitle><description>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.
Deep-learning models predict the Cas9 variant with optimal activity and specificity for any target sequence.</description><subject>631/1647/1511</subject><subject>631/1647/1513/1967/3196</subject><subject>Agriculture</subject><subject>Base Sequence</subject><subject>Bioinformatics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Cleavage</subject><subject>Computer applications</subject><subject>CRISPR-Associated Protein 9 - genetics</subject><subject>Deep Learning</subject><subject>Forecasts and trends</subject><subject>Gene Library</subject><subject>Genomes</subject><subject>HEK293 Cells</subject><subject>Humans</subject><subject>Identification and classification</subject><subject>INDEL Mutation - genetics</subject><subject>Intraspecific genetic variation</subject><subject>Learning</subject><subject>Lentivirus - genetics</subject><subject>Life Sciences</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Medical schools</subject><subject>Medicine</subject><subject>Models, Genetic</subject><subject>Mutation - genetics</subject><subject>Nucleotides</subject><subject>Streptococcus pyogenes</subject><subject>Technology application</subject><subject>Transcription factors</subject><subject>Transfer RNA</subject><subject>University 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Kwon</au><au>Lee, Sungtae</au><au>Seo, Jung Hwa</au><au>Choi, Jae Woo</au><au>Park, Jinman</au><au>Min, Seonwoo</au><au>Yoon, Sungroh</au><au>Cho, Sung-Rae</au><au>Kim, Hyongbum Henry</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of the sequence-specific cleavage activity of Cas9 variants</atitle><jtitle>Nature biotechnology</jtitle><stitle>Nat Biotechnol</stitle><addtitle>Nat Biotechnol</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>38</volume><issue>11</issue><spage>1328</spage><epage>1336</epage><pages>1328-1336</pages><issn>1087-0156</issn><eissn>1546-1696</eissn><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.
Deep-learning models predict the Cas9 variant with optimal activity and specificity for any target sequence.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>32514125</pmid><doi>10.1038/s41587-020-0537-9</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0694-9244</orcidid><orcidid>https://orcid.org/0000-0002-4693-738X</orcidid><orcidid>https://orcid.org/0000-0002-2367-197X</orcidid><orcidid>https://orcid.org/0000-0002-8489-7972</orcidid></addata></record> |
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| subjects | 631/1647/1511 631/1647/1513/1967/3196 Agriculture Base Sequence Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cleavage Computer applications CRISPR-Associated Protein 9 - genetics Deep Learning Forecasts and trends Gene Library Genomes HEK293 Cells Humans Identification and classification INDEL Mutation - genetics Intraspecific genetic variation Learning Lentivirus - genetics Life Sciences Machine learning Mathematical models Medical schools Medicine Models, Genetic Mutation - genetics Nucleotides Streptococcus pyogenes Technology application Transcription factors Transfer RNA University colleges Varieties |
| title | Prediction of the sequence-specific cleavage activity of Cas9 variants |
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