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Machine learning predicts new anti-CRISPR proteins
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Eitzinger, Simon, Asiff, Amina, Watters, Kyle E., Iavarone, Anthony T., Knott, Gavin J., Doudna, Jennifer A. and Minhas, Fayyaz ul Amir Afsar (2020) Machine learning predicts new anti-CRISPR proteins. Nucleic Acids Research, 48 (9). pp. 4698-4708. doi:10.1093/nar/gkaa219
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WRAP-Machine-learning-predicts-new-anti-CRISPR-proteins-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (1216Kb) | Preview |
Official URL: https://doi.org/10.1093/nar/gkaa219
Abstract
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/.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QH Natural history |
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| Divisions: | Faculty of Science > Computer Science | ||||||||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | CRISPR-associated protein 9, CRISPR-associated protein 9 -- Data processing , CRISPR (Genetics) -- Data processing , Gene editing, Machine learning | ||||||||||||||||||
| Journal or Publication Title: | Nucleic Acids Research | ||||||||||||||||||
| Publisher: | Oxford University Press (OUP) | ||||||||||||||||||
| ISSN: | 1362-4962 | ||||||||||||||||||
| Official Date: | 21 May 2020 | ||||||||||||||||||
| Dates: |
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| Volume: | 48 | ||||||||||||||||||
| Number: | 9 | ||||||||||||||||||
| Page Range: | pp. 4698-4708 | ||||||||||||||||||
| DOI: | 10.1093/nar/gkaa219 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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