Machine learning predicts new anti-CRISPR proteins
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/.
- Authors:
-
[3];
- Univ. of California, Berkeley, CA (United States)
- Pakistan Institute of Engineering and Applied Sciences (PIEAS), PO Nilore, Islamabad (Pakistan); National University of Computer and Emerging Sciences (NUCES), Islamabad (Pakistan). FAST School of Computing
- Univ. of California, Berkeley, CA (United States). Innovative Genomics Institute and Howard Hughes Medical Institute; Gladstone Institutes, San Francisco, CA (United States). Institute of Data Science and Biotechnology; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Pakistan Institute of Engineering and Applied Sciences (PIEAS), PO Nilore, Islamabad (Pakistan); University of Warwick, Coventry (United Kingdom)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); Defense Advanced Research Projects Agency (DARPA); National Science Foundation (NSF); The Paul G. Allen Frontiers Group; National Institutes of Health (NIH)
- OSTI Identifier:
- 1633284
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nucleic Acids Research
- Additional Journal Information:
- Journal Volume: 48; Journal Issue: 9; Journal ID: ISSN 0305-1048
- Publisher:
- Oxford University Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Eitzinger, Simon, Asif, Amina, Watters, Kyle E., Iavarone, Anthony T., Knott, Gavin J., Doudna, Jennifer A., and Minhas, Fayyaz ul Amir Afsar. Machine learning predicts new anti-CRISPR proteins. United States: N. p., 2020.
Web. doi:10.1093/nar/gkaa219.
Eitzinger, Simon, Asif, Amina, Watters, Kyle E., Iavarone, Anthony T., Knott, Gavin J., Doudna, Jennifer A., & Minhas, Fayyaz ul Amir Afsar. Machine learning predicts new anti-CRISPR proteins. United States. https://doi.org/10.1093/nar/gkaa219
Eitzinger, Simon, Asif, Amina, Watters, Kyle E., Iavarone, Anthony T., Knott, Gavin J., Doudna, Jennifer A., and Minhas, Fayyaz ul Amir Afsar. Tue .
"Machine learning predicts new anti-CRISPR proteins". United States. https://doi.org/10.1093/nar/gkaa219. https://www.osti.gov/servlets/purl/1633284.
@article{osti_1633284,
title = {Machine learning predicts new anti-CRISPR proteins},
author = {Eitzinger, Simon and Asif, Amina and Watters, Kyle E. and Iavarone, Anthony T. and Knott, Gavin J. and Doudna, Jennifer A. and Minhas, Fayyaz ul Amir Afsar},
abstractNote = {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/.},
doi = {10.1093/nar/gkaa219},
journal = {Nucleic Acids Research},
number = 9,
volume = 48,
place = {United States},
year = {Tue Apr 14 00:00:00 EDT 2020},
month = {Tue Apr 14 00:00:00 EDT 2020}
}
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Table 1: Results for leave-one-out cross-validation
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