ArticleNucleic acids research2020
Machine learning predicts new anti-CRISPR proteins.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 2 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- AcrSeek: metric learning with hybrid negative mining for anti-CRISPR protein detection under extreme class imbalance.Bioinformatics (Oxford, England) · 2026Article
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- SauCas9-based cell cycle-dependent genome editing via AAV delivery.Molecular therapy. Advances · 2026Article
- Novel Silviavirus phages with broad-host-range activity against methicillin resistant Staphylococcus aureus from seafood: comprehending the phenotypic and genotypic variability.Folia microbiologica · 2026Article
- Investigating the molecular mechanisms underlying the anti-CRISPR function of AcrIIA13b protein.The FEBS journal · 2026Article
- AcrIIA7 hijacks tracrRNA to block CRISPR-Cas system.Nature communications · 2026Article
- The anti-CRISPR protein AcrIE8.1 inhibits the type I-E CRISPR-Cas system by directly binding to the Cascade subunit Cas11.FEBS letters · 2026Article
- Isolation, characterization and genomic analysis of a novel lytic bacteriophage EcoPhCCP1, capable of infecting multiple strains of multidrug-resistantThe Journal of general virology · 2026Article
- Article
- Comparing Neural Networks and Naive Bayes in the Prediction of Drug Gene Interactions of Type 4 Collagenase for Gingival Epithelialization.International journal of dentistry · 2026Article
- Artificial Intelligence Applications in Bacteriophage Genomics and Host Interaction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- NRG-P0074 Viral Sample RU1 from UnclassifiedPHAGE (New Rochelle, N.Y.) · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- AcrDB update: Predicted 3D structures of anti-CRISPRs in human gut viromes.Protein science : a publication of the Protein Society · 2025Article
- Artificial Intelligence and Computational Biology in Gene Therapy: A Review.Biochemical genetics · 2025Review
- Engineering stimuli-responsive CRISPR-Cas systems for versatile biosensing.Analytical and bioanalytical chemistry · 2025Review
- Synergizing CRISPR-Cas9 with Advanced Artificial Intelligence and Machine Learning for Precision Drug Delivery: Technological Nexus and Regulatory Insights.Current gene therapy · 2025Review
- Isolation and Characterization of Lytic Bacteriophages Capable of Infecting Diverse Multidrug-Resistant Strains ofPharmaceuticals (Basel, Switzerland) · 2024Article
4 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
7 authors at 3 institutions in 3 countries.
Funding
Abstract
The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.