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

Wenhui Li, Xianyue Jiang, Wuke Wang, Liya Hou, Runze Cai, Yongqian Li, Qiuxi Gu, Qinchang Chen, Peixiang Ma, Jin Tang, Menghao Guo, Guohui Chuai (), Xingxu Huang (), Jun Zhang () and Qi Liu ()
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Wenhui Li: Tongji University
Xianyue Jiang: Zhejiang Lab
Wuke Wang: Zhejiang Lab
Liya Hou: Zhejiang Lab
Runze Cai: Zhejiang Lab
Yongqian Li: Zhejiang Lab
Qiuxi Gu: Nanjing Medical University
Qinchang Chen: Zhejiang Lab
Peixiang Ma: Shanghai Jiao Tong University School of Medicine
Jin Tang: Zhejiang Lab
Menghao Guo: Zhejiang Lab
Guohui Chuai: Tongji University
Xingxu Huang: Zhejiang Lab
Jun Zhang: Nanjing Medical University
Qi Liu: Tongji University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract The discovery of CRISPR-Cas systems has paved the way for advanced gene editing tools. However, traditional Cas discovery methods relying on sequence similarity may miss distant homologs and aren’t suitable for functional recognition. With protein large language models (LLMs) evolving, there is potential for Cas system modeling without extensive training data. Here, we introduce CHOOSER (Cas HOmlog Observing and SElf-processing scReening), an AI framework for alignment-free discovery of CRISPR-Cas systems with self-processing pre-crRNA capability using protein foundation models. By using CHOOSER, we identify 11 Casλ homologs, nearly doubling the known catalog. Notably, one homolog, EphcCasλ, is experimentally validated for self-processing pre-crRNA, DNA cleavage, and trans-cleavage, showing promise for CRISPR-based pathogen detection. This study highlights an innovative approach for discovering CRISPR-Cas systems with specific functions, emphasizing their potential in gene editing.

Date: 2024
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DOI: 10.1038/s41467-024-54365-0

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