通过超基因组采矿和机器学习揭示Cas9 PAM多样性
Tao Fang1, Lea Bogensperger2, Lilith Feer2
1Institute of Pharmacology and Toxicology, University of Zurich, Zürich, Switzerland.
Nature communications
|February 8, 2026
概括
我们开发了CRISPR-PAMdb和CICERO,以发现新的CRISPR-Cas9 PAM序列. 这扩大了基因组编辑能力,通过识别具有多种PAM兼容性的新型Cas9正义基因组.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 对于CRISPR-Cas系统来说,原生空间器相邻动机 (PAM) 的识别是必不可少的.
- 目前Cas9变种的有限PAM兼容性限制了基因组编辑应用.
- 发现具有扩展PAM识别的新型Cas9基因对象至关重要.
研究的目的:
- 创建一个关于Cas9蛋白及其PAM配置文件的全面数据库.
- 开发一种机器学习模型,从Cas9序列中预测PAM偏好.
- 促进用于精确基因组工程的新CRISPR-Cas9工具的发现.
主要方法:
- 从数以百万计的基因组中编译了Cas9序列,并从菌体/质粒序列中编译了PAM档案,并将其转化为CRISPR-PAMdb.
- 推断的共识 PAM 偏好使用 8003 Cas9 集群的间隔器-原间隔器对齐.
- 开发了基于ESM2的机器学习模型CICERO,用于从Cas9序列中预测PAM.
主要成果:
- CRISPR-PAMdb包含了大量的Cas9和PAM数据.
- 基于对齐的方法确定了8003个Cas9集群的PAM.
- CICERO预测了50,308个额外的Cas9蛋白质的PAM,在经过验证的正统对象上实现了高精度.
结论:
- CRISPR-PAMdb和CICERO使得Cas9 PAM多样性的大规模探索成为可能.
- 这些资源加速了下一代CRISPR-Cas9工具的设计.
- 这些发现支持在基因组工程应用中提高精度.
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