PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins

Abstract

Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR–Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR–Cas machineries for the development of new CRISPR–Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.

AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses

“…Acr-Aca operons containing top 10% ranked proteins are considered to be more confident candidates according to the AcRanker paper ( 23 ). Meanwhile, after the AcrFinder step, all the 57 879 proteins (unique IDs) of the predicted Acr-Aca operons were processed by PaCRISPR to receive a prediction score (Figure 1 ) and those with a score >0.5 were considered as more confident candidates according to the PaCRISPR paper ( 24 ).…”

“…Additionally, in the year 2020, four standalone/webserver bioinformatics tools become available for automated Acr discovery (Table 1 ). Three of these tools were developed by authors of this paper: AcrFinder ( 22 ), AcRanker ( 23 ) and PaCRISPR ( 24 ). AcrFinder implemented a bioinformatics pipeline that was reported in ( 25 ), where we developed a bioinformatics pipeline to identify genomic operons containing Acr homologs and/or Aca homologs by combining three computational approaches: homology, GBA (guilt-by-association), and STS (self-targeting spacer) ( 26 ).…”

“…AcRanker uses amino acid compositions of known Acr and non-Acr proteins to train an Extreme Gradient Boosting (XGBoost) ranking algorithm to rank putative Acr proteins in a given test proteome, and has been used to assist experimental discovery of novel Acr proteins ( 23 ). PaCRISPR builds position-specific scoring matrices (PSSMs) to capture evolutionary features in Acr alignments, and then trains a support vector machine (SVM) for Acr classification ( 24 ). All these three recent tools have their strengths and weaknesses, and we were motivated to combine predictions from three independent tools to create a comprehensive Acr-Aca operon database with a user-friendly website.…”

“…Briefly, combining AcrFinder ( 22 ), AcRanker ( 23 ), and PaCRISPR ( 24 ), we have performed a large-scale bioinformatics data mining for putative Acr-Aca operons in all sequenced genomes of bacteria, archaea, and their viruses. As a result, a new pre-computed Acr-Aca operon database AcrDB was developed.…”

AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins

“…Secondly, AcrFinder, which integrates homology-based, guilt-by-association-based and self-targeting approaches to identify potential Acrs from prokaryotes and phage genomes ( 22 ). Thirdly, PaCRISPR ( 23 ), which incorporates evolutionary information within an ensemble model to predict Acr proteins from genomic and metagenomic sequences. However, these toolkits are prediction-oriented and have limited downstream capabilities for further exploring those predicted Acrs.…”