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

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.

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Publication Details

Journal
Nature Communications
Published
2024-11-19
DOI
https://doi.org/10.1038/s41467-024-54365-0
Citations
15
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
3.51

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article

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

Peixiang Ma, Guohui Chuai, Wuke Wang, Xingxu Huang et al.
15 citations
Nature Communications
CRISPR and Genetic Engineering
3.51
article

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

Peixiang Ma, Guohui Chuai, Wuke Wang, Xingxu Huang, Wenhui Li, Jin Tang, Runze Cai, Yongqian Li, Jun Zhang, Qi Liu, Meng-Hao Guo, Qiuxi Gu, Qinchang Chen, Liya Hou, Xianyue Jiang
article en
15 citations

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.

Nature CommunicationsVol. 15(1)
Tongji University (CN), Shanghai Jiao Tong University (CN), Shanghai Ninth People's Hospital (CN), Nanjing Maternity and Child Health Care Hospital (CN), Intelligent Systems Research (United States) (US), Zhejiang Lab (CN), Shanghai East Hospital (CN), Tongji Hospital (CN), Center for Life Sciences (CN), First Affiliated Hospital Zhejiang University (CN), Zhejiang University (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 6%
CRISPR and Genetic Engineering
3.51
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