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DefensePredictor: A machine learning model to discover prokaryotic immune systems.

Peter C DeWeirdt1,2, Emily M Mahoney1,3, Michael T Laub1,4

  • 1Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.

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DefensePredictor, a new machine learning model, identifies novel antiphage defense systems in bacteria. This discovery expands our understanding of bacterial immunity and aids biotechnology development.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Immunology

Background:

  • Antiphage defense systems are crucial for bacterial survival against viral infections.
  • These systems have implications for understanding eukaryotic innate immunity and developing biotechnologies like CRISPR-Cas9.
  • Current discovery methods, like genomic colocalization, are limited to identifying systems within defense islands.

Purpose of the Study:

  • To develop a machine learning model, DefensePredictor, for identifying antiphage defense systems.
  • To discover previously unknown antiphage defense systems in prokaryotes.
  • To explore the uncharacterized defense repertoire in prokaryotic genomes.

Main Methods:

  • Utilized protein language model embeddings for classifying proteins.
  • Developed DefensePredictor, a machine learning model for defense protein prediction.
  • Applied DefensePredictor to 69 diverse *Escherichia coli* strains and 1000 prokaryotic genomes.

Main Results:

  • Predicted hundreds of novel antiphage defense systems in *E. coli*.
  • Experimentally validated 42 of the predicted systems.
  • Identified nearly 3000 protein clusters without homology to known systems across 1000 prokaryotic genomes.

Conclusions:

  • DefensePredictor is effective in discovering novel antiphage defense systems.
  • A vast, uncharacterized repertoire of bacterial defense mechanisms exists.
  • This work will advance the discovery of antiphage systems, revealing links between prokaryotic and eukaryotic immunity and accelerating biotechnology.