通过基础模型发现具有自我加工前体crRNA能力的CRISPR-Cas系统。
Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models.
作者信息
Li Wenhui, Jiang Xianyue, Wang Wuke, Hou Liya, Cai Runze, Li Yongqian, Gu Qiuxi, Chen Qinchang, Ma Peixiang, Tang Jin, Guo Menghao, Chuai Guohui, Huang Xingxu, Zhang Jun, Liu Qi
机构信息
State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
出版信息
Nat Commun. 2024 Nov 19;15(1):10024. doi: 10.1038/s41467-024-54365-0.
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.
CRISPR-Cas系统的发现为先进的基因编辑工具铺平了道路。然而，传统的基于序列相似性的Cas发现方法可能会遗漏远源同源物，不适用于功能识别。随着蛋白质大语言模型（LLMs）的发展，无需大量训练数据即可对Cas系统进行建模。在这里，我们介绍了CHOOSER（Cas同源物观察与自处理筛选），这是一个使用蛋白质基础模型无比对发现具有自处理前体crRNA能力的CRISPR-Cas系统的人工智能框架。通过使用CHOOSER，我们鉴定出11个Casλ同源物，使已知的目录几乎增加了一倍。值得注意的是，其中一个同源物EphcCasλ经过实验验证具有自处理前体crRNA、DNA切割和反式切割的能力，显示出基于CRISPR的病原体检测的潜力。这项研究突出了一种发现具有特定功能的CRISPR-Cas系统的创新方法，强调了它们在基因编辑中的潜力。
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