Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases

  • Katharina G. Wandera
  • , Omer S. Alkhnbashi
  • , Harris v.I. Bassett
  • , Alexander Mitrofanov
  • , Sven Hauns
  • , Anzhela Migur
  • , Rolf Backofen*
  • , Chase L. Beisel*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against type VI-B CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and subtypes, including over 7,000 putative type IV and VI Acrs not predicted by other algorithms. By performing a cell-free screen for Acr hits against type VI-B systems, we identified a potent inhibitor of Cas13b nucleases we named AcrVIB1. AcrVIB1 blocks Cas13b-mediated defense against a targeted plasmid and lytic phage, and its inhibitory function principally occurs upstream of ribonucleoprotein complex formation. Overall, our work helps expand the known Acr universe, aiding our understanding of the bacteria-phage arms race and the use of Acrs to control CRISPR technologies.

Original languageEnglish
Pages (from-to)2714-2726.e4
JournalMolecular Cell
Volume82
Issue number14
DOIs
StatePublished - 21 Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Inc.

ASJC Scopus subject areas

  • Molecular Biology
  • Cell Biology

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