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AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins.

Fu-Ying Dao1, Meng-Lu Liu2, Wei Su2

  • 1Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore.

International Journal of Biological Macromolecules
|December 30, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed AcrPred, a highly accurate tool for predicting Anti-CRISPR (Acr) proteins. This gene editing advancement aids in regulating CRISPR-Cas systems and identifying new Acr proteins.

Keywords:
Anti-CRISPR proteinMachine learningWeb-server

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Area of Science:

  • Molecular Biology
  • Biotechnology
  • Bioinformatics

Background:

  • CRISPR-Cas systems are powerful gene editing tools.
  • Anti-CRISPR (Acr) proteins inhibit CRISPR-Cas activity, offering regulatory potential.
  • Understanding Acr proteins is crucial for advancing gene editing applications.

Purpose of the Study:

  • To develop a highly accurate prediction model for identifying Anti-CRISPR proteins.
  • To create a user-friendly web server for researchers to predict Acr proteins.

Main Methods:

  • A two-step model fusion strategy was employed to build the AcrPred model.
  • The model's performance was validated using an independent dataset.
  • Comparison with existing tools and testing on novel Acr proteins were conducted.

Main Results:

  • The AcrPred model achieved a high accuracy, with an AUC of 0.952 on an independent dataset.
  • The model demonstrated strong generalization ability, correctly identifying 9 out of 10 new Acr proteins.
  • A user-friendly web server (AcrPred) was established for easy identification of potential Acr proteins.

Conclusions:

  • AcrPred is a robust and accurate tool for predicting Anti-CRISPR proteins.
  • The developed web server facilitates research in gene editing regulation.
  • This work significantly contributes to the understanding and application of Anti-CRISPR proteins in gene editing.