PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

Lin Zhu, Xiaoyu Wang, Fuyi Li, Jiangning Song

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


Background: Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. Results: Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. Conclusions: In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at:

Original languageEnglish
Article number444
Number of pages21
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - Dec 2022


  • Anti-CRISPR protein
  • Feature engineering
  • Machine learning
  • Sequence analysis

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