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, Meng-Hao Guo, Guohui Chuai, Xingxu Huang, Jun Zhang, Qi Liu
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. Identification of CRISPR-Cas systems by sequence alignment is limited by homolog sequence diversity. Here, the authors develop CHOOSER, and AI framework, for alignment free discovery of CRISPR-Cas systems, including EphcCasλ, which was validated for self-processing and DNA cleavage.