ArticleNature biotechnology2020
Prediction of the sequence-specific cleavage activity of Cas9 variants.
Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 1 of them a synthesis that pooled it.
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Who cites it
132 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Structure-guided discovery and engineering of natural Cas9 orthologs for high-fidelity editing.Science China. Life sciences · 2026Article
- Advances in prime editing: Molecular innovations, Large-fragment engineering, and AI-driven design.Biodesign research · 2026Review
- Article
- Using enantioselective biosensors to evolve asymmetric biocatalysts.Nature chemical biology · 2026Article
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- High-throughput evaluation of in vitro CRISPR activities enables optimized large-scale multiplex enrichment of rare variants.Nature biomedical engineering · 2026Article
- Wheat's Up with CRISPR-Cas-Current Advances, Obstacles and Perspectives.International journal of molecular sciences · 2026Review
- High-fidelity genome and prime editing enabled by the AI-designed openCRISPR-1.Genome medicine · 2026Article
- Engineering Compact Base Editors by AlphaFold-Guided Mutation Scan and Escherichia coli-Based Tri-Selection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cas9-Embedding Hyperactive TadA8e Confers Efficient and Highly Specific A-To-G Base Editing in Rice.Plant biotechnology journal · 2026Article
- A multi-kingdom genetic barcoding system for precise clone isolation.Nature biotechnology · 2026Article
- GenomePAM directs PAM characterization and engineering of CRISPR-Cas nucleases using mammalian genome repeats.Nature biomedical engineering · 2026Article
- Artificial Intelligence-Assisted CRISPR Gene Editing: Current Advances, Clinical Challenges, and Future Directions in Precision Medicine.Avicenna journal of medical biotechnology · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Cooperative Mismatch Discrimination by PNA and DNAzyme Enables High-Fidelity Cleavage of Plasmid DNA.Biochemistry · 2025Article
- The editable landscape of the yeast genome reveals hotspots of structural variant formation.Science advances · 2025Article
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.Bioinformatics (Oxford, England) · 2025Article
- A rational engineering strategy for structural dynamics modulation enables target specificity enhancement of the Cas9 nuclease.Nucleic acids research · 2025Article
72 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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