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.

Specificities and functional coordination between the two Cas6 maturation endonucleases in Anabaena sp. PCC 7120 assign orphan CRISPR arrays to three groups

“…Such inactivation could result from mutations or neutralization by an anti-CRISPR factor. Anti-CRISPR proteins have been characterized for several different types of CRISPR-Cas systems and have also been predicted for Type III-D systems (Gussow et al, 2020).…”

CRISPR-Cas is associated with fewer antibiotic resistance genes in bacterial pathogens

“…A BLAST database of type I-C anti-CRISPR (acr) genes was constructed based on protein sequences identified in [34][35][36]. Protein coding regions of all P. aeruginosa genomes were predicted using Prodigal v. 2.6.3 [37].…”

A Type I-F Anti-CRISPR Protein Inhibits the CRISPR-Cas Surveillance Complex by ADP-Ribosylation

“…To effectively evade CRISPR systems, bacteriophages have evolved protein-based inhibitors, called anti-CRISPRs (Acrs), to inactivate the CRISPR-Cas immune systems (Koonin and Makarova, 2018;Marino et al, 2020;Pawluk et al, 2018). Studies have identified a large number of Acrs, targeting type I (Bondy- Denomy et al, 2013;He et al, 2018;Marino et al, 2018;Pawluk et al, 2014Pawluk et al, , 2016b, type II (Gussow et al, 2020;Eitzinger et al, 2020;Forsberg et al, 2019;Harrington et al, 2017;Hynes et al, 2017Hynes et al, , 2018Lee et al, 2018;Liu et al, 2019;Mahendra et al, 2020;Pawluk et al, 2016a;Rauch et al, 2017;Uribe et al, 2019;Watters et al, 2020), type III (Athukoralage et al, 2020;Bhoobalan-Chitty et al, 2019), type V (Marino et al, 2018;Watters et al, 2018), and recently type VI (Lin et al, 2020) CRISPR-Cas systems. For the type I CRISPR systems, 14 Acrs were described to target the type I-F system (i.e., AcrIF1-IF14), 7 Acrs were found to exclusively target the type I-E system (i.e., AcrIE1-IE7), 1 Acr targeted type I-C (i.e., AcrIC1), and 1 Acr targeted type I-D (i.e., AcrID1) (Hwang and Maxwell, 2019).…”