使用大型语言模型发现CRISPR-Cas12a基团
Yuanyuan Feng1, Junchao Shi1, Zhanwei Li1
1Research Center for Life Sciences computing, Zhejiang Lab, Hangzhou, China.
Nature communications
|August 23, 2025
概括
这项研究使用语言模型发现新的CRISPR-Cas蛋白及其功能,从而实现新的基因编辑和疾病检测策略.
科学领域:
- 基因组学
- 分子生物学
- 生物信息学
背景情况:
- CRISPR-Cas系统是生命科学中的强大工具,
- 识别Cas蛋白的传统方法依赖于序列对齐,限制了发现.
- 对基因编辑技术的发展至关重要.
研究的目的:
- 利用进化规模语言模型 (ESM) 来识别和描述从元基因组数据中未知的Cas蛋白质.
- 通过将ESM与机器学习集成来预测未表征的Cas12a蛋白的跨裂变活性.
- 探索新发现的Cas蛋白亚型的结构和功能多样性.
主要方法:
- 使用在CRISPR-Cas数据上训练的进化规模语言模型 (ESM) 来识别无对齐的Cas蛋白.
- 将机器学习与ESM集成,用于预测未表征的Cas12a的跨裂变活动.
- 使用结构分析和冷电子显微镜 (CryoEM) 来研究蛋白质结构和RNA相互作用.
- 分类基因集群以探索未经记录的Cas蛋白功能.
主要成果:
- 在不依赖序列对齐的情况下使用ESM准确识别Cas蛋白.
- 发现7种未经证实的Cas12a亚型, 具有独特的CRISPR位点和独特的3D折叠.
- 鉴定了Cas1,Cas2和Cas4蛋白的8个亚型.
- 揭示独特的RNA相互作用和独特的DNA裂变偏好 (双链和单链),用于新型Cas12a亚型的广泛PAM识别.
- 使用新型Cas12a PAM开发一种针对癌基SNP的特定检测策略.
结论:
- 进化规模语言模型提供了一种强大的方法来发现和功能性地注释在元基因组中的未表征的Cas蛋白质.
- 发现的Cas12a亚型具有独特的生化特性和结构特征,扩大了CRISPR工具包.
- 这项工作展示了人工智能驱动的方法在加速探索微生物多样性及其生物技术应用方面的潜力.
- 开发的检测策略突显了使用新型CRISPR-Cas系统进行向疾病诊断的潜力.
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