工程CRISPR-Cas PAM识别使用深度学习的巨大进化数据的工程
Stephen Nayfach1, Aadyot Bhatnagar1, Andrey Novichkov1
1Profluent Bio, Berkeley, CA, USA.
bioRxiv : the preprint server for biology
|January 20, 2025
概括
Protein2PAM是一个深度学习模型,准确地预测了CRISPR-Cas PAM的特异性,并为个性化基因组编辑设计了Cas酶. 这通过启用定制PAM识别来推进基因编辑.
科学领域:
- 生物技术是生物技术.
- 基因组学就是基因组学.
- 分子生物学分子生物学
背景情况:
- 对于基因组向,CRISPR-Cas酶需要对原体空间邻动机 (PAM) 的识别.
- PAM的特异性限制了CRISPR-Cas系统可以编辑的序列范围.
- 蛋白质工程提供了一条定制Cas酶用于新型PAM识别的途径.
研究的目的:
- 开发一个人工智能驱动的工具,Protein2PAM,用于预测Cas蛋白PAM特异性.
- 使用Protein2PAM来设计具有改变PAM识别能力的Cas酶.
- 展示机器学习在定制CRISPR-Cas系统中的应用,用于基因组编辑.
主要方法:
- 开发了Protein2PAM,这是一个以进化为基础的深度学习模型,在超过45,000个CRISPR-Cas PAM序列上训练.
- 应用了深度突变扫描以识别PAM识别的关键残留物在Cas9.
- 使用Protein2PAM扩大PAM识别的Nme1Cas9变体的计算进化.
主要成果:
- Protein2PAM准确地预测了在I型,II型和V型CRISPR-Cas系统中的PAM特异性.
- 在Cas9中识别了PAM识别的关键残留物,而不依赖结构数据.
- 产生的Nme1Cas9变体具有扩展的PAM识别和高达50倍的分裂率in vitro.
结论:
- Protein2PAM是机器学习的第一个成功应用,用于定制Cas酶PAM识别.
- 这种方法可以为特定的个性化基因组编辑应用程序设计Cas酶.
- 这些发现为使用CRISPR-Cas技术进行更广泛,更精确的基因组向铺平了道路.
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