Vast diversity of anti-CRISPR proteins predicted with a machine-learning approach

Abstract

Bacteria and archaea evolve under constant pressure from numerous, diverse viruses and thus have evolved multiple defense systems. The CRISPR-Cas are adaptive immunity systems that have been harnessed for the development of the new generation of genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechanisms including numerous, diverse anti-CRISPR proteins (Acrs) that can inhibit CRISPR-Cas and therefore have enormous potential for application as modulators of genome editing tools. Most Acrs are small, highly variable proteins which makes their prediction a formidable task. We developed a machine learning approach for comprehensive Acr prediction. The model showed high predictive power when tested against an unseen test set that included several families of recently discovered Acrs and was employed to predict 2,500 novel candidate Acr families. An examination of the top candidates confirms that they possess typical Acr features. One of the top candidates was independently tested and found to possess anti-CRISPR activity (AcrIIA12). We provide a web resource (http://acrcatalog.pythonanywhere.com/) to access the predicted Acrs sequences and annotation. The results of this analysis expand the repertoire of predicted Acrs almost by two orders of magnitude and provide a rich resource for experimental Acr discovery.