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

Jiawei Wang, Wei Dai, Jiahui Li, Ruopeng Xie, Rhys A. Dunstan, Christopher Stubenrauch, Yanju Zhang, Trevor Lithgow

Research output: Contribution to journalArticleResearchpeer-review

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/.

Original languageEnglish
Pages (from-to)W348-W357
Number of pages10
JournalNucleic Acids Research
Volume48
Issue numberW1
DOIs
Publication statusPublished - 27 May 2020

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