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Updated: Jun 7, 2025

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Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models.

Wenhui Li1,2,3, Xianyue Jiang3, Wuke Wang3

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|November 20, 2024
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Summary
This summary is machine-generated.

This study introduces CHOOSER, an AI framework for discovering CRISPR-Cas systems. It identified 11 new Casλ homologs, including one with self-processing capabilities for potential pathogen detection.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • CRISPR-Cas systems are powerful gene editing tools.
  • Traditional discovery methods miss distant homologs and lack functional recognition.
  • Protein large language models (LLMs) offer new possibilities for Cas system modeling.

Purpose of the Study:

  • To develop an AI framework for alignment-free discovery of CRISPR-Cas systems.
  • To identify novel CRISPR-Cas homologs with self-processing pre-crRNA capabilities.
  • To explore the potential of these systems in gene editing and diagnostics.

Main Methods:

  • Utilized CHOOSER (Cas HOmlog Observing and SElf-processing scReening), an AI framework employing protein foundation models.
  • Performed alignment-free screening for CRISPR-Cas systems.
  • Employed experimental validation for identified homologs.

Main Results:

  • Identified 11 Casλ homologs, nearly doubling the known catalog.
  • Discovered and experimentally validated EphcCasλ for self-processing pre-crRNA, DNA cleavage, and trans-cleavage.
  • Demonstrated the efficacy of CHOOSER in discovering functional CRISPR-Cas systems.

Conclusions:

  • CHOOSER provides an innovative approach for discovering CRISPR-Cas systems with specific functions.
  • The identified EphcCasλ homolog shows significant promise for CRISPR-based pathogen detection.
  • This work expands the catalog of CRISPR-Cas systems and highlights their potential in advanced biotechnological applications.