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Machine learning predicts new anti-CRISPR proteins.

Simon Eitzinger1, Amina Asif2,3, Kyle E Watters1

  • 1Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720, USA.

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|April 15, 2020
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Summary
This summary is machine-generated.

Researchers developed AcRanker, a machine learning tool to identify new anti-CRISPR proteins using only sequence data. This method accelerates the discovery of novel CRISPR-Cas inhibitors for gene editing control.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • CRISPR-Cas9 technology is widely used in medicine, agriculture, and synthetic biology.
  • Discovering CRISPR-Cas inhibitors (anti-CRISPRs) is crucial for controlling gene editing applications.
  • Existing anti-CRISPRs lack identifiable shared properties for easy bioinformatic discovery.

Purpose of the Study:

  • To develop a machine learning method for identifying novel anti-CRISPRs using protein sequences.
  • To enable faster discovery and validation of anti-CRISPR candidates.

Main Methods:

  • Developed AcRanker, a machine learning model based on XGBoost ranking.
  • Trained the model on a dataset of known anti-CRISPR proteins.
  • Applied AcRanker to predict anti-CRISPRs from bacterial prophage regions.

Main Results:

  • Identified two novel anti-CRISPRs: AcrIIA20 and AcrIIA21.
  • AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9).
  • AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9), and SinCas9 with low potency.

Conclusions:

  • AcRanker effectively identifies novel anti-CRISPRs from protein sequences.
  • The tool aids in ranking candidate anti-CRISPR genes, accelerating research.
  • AcRanker is available as a web server for broader accessibility.