Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 2, 2025

Abstract

MOTIVATION: Accurate prediction of single-guide RNA (sgRNA) activity is crucial for optimizing the CRISPR/Cas9 gene-editing system, as it directly influences the efficiency and accuracy of genome modifications. However, existing prediction methods mainly rely on large-scale experimental data of a single Cas9 variant to construct Cas9 protein (variants)-specific sgRNA activity prediction models, which limits their generalization ability and prediction performance across different Cas9 protein (variants), as well as their scalability to the continuously discovered new variants.

Authors

  • Yalin Hou
    School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Ruiqing Zheng
  • Fuhao Zhang
    School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China.
  • Fei Guo
    School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address: gfjy001@yahoo.com.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Min Zeng
    Nephrology Department, Affiliated Hospital of Southern Medical University: Shenzhen Longhua New District People's Hospital, Shenzhen, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals