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Updated: Feb 4, 2026

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Substrate Generation for Endonucleases of CRISPR/Cas Systems
Published on: September 8, 2012
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通过蛋白质语言模型定制CRISPR-Cas PAM特异性
Stephen Nayfach1, Aadyot Bhatnagar2, Andrey Novichkov2
1Profluent Bio, Emeryville, CA, USA. snayfach@profluent.bio.
Nature biotechnology
|February 2, 2026
概括
科学家们开发了Protein2PAM,这是一种深度学习模型,可以有效地为特定的DNA目标设计CRISPR-Cas变体. 这推动了基因组编辑的进步,克服了原空间体相邻的动图限制.
科学领域:
- 基因编辑技术CRISPR-Cas是一种基因编辑技术.
- 生物信息学和计算生物学
- 蛋白质工程和定向进化研究
背景情况:
- 克里斯普尔-卡斯系统需要原体空间相邻的基因 (PAM) 来定位DNA,限制可编辑基因组位点的范围.
- 目前用于工程PAM特异性的方法往往是费力和耗时的,涉及代的实验步骤.
研究的目的:
- 引入一个以进化为基础的深度学习模型,Protein2PAM,用于高效设计具有改变PAM特异性的Cas蛋白变体.
- 通过克服CRISPR-Cas系统中的PAM约束来实现以前无法访问的基因组序列的精确准.
主要方法:
- 在CRISPR-Cas PAM序列的大数据集 (>45,000) 上开发和训练了Protein2PAM.
- 使用in silico mutagenesis来识别Cas9中PAM识别的关键残留物,而不依赖结构数据.
- 使用Protein2PAM用于Nme1Cas9变体的计算进化.
主要成果:
- 蛋白2PAM准确地预测了各种CRISPR-Cas类型 (I,II,V) 的PAM特异性.
- 在Cas9中使用计算方法识别PAM识别的关键残留物.
- 产生了Nme1Cas9变异,显著扩大了PAM识别,并且在体外裂解率增加了50倍.
结论:
- Protein2PAM提供了一种高效的,以机器学习为驱动的方法,用于为特定的PAM识别设计CRISPR-Cas酶.
- 这项技术扩大了CRISPR-Cas系统的准能力,提高了个性化基因组编辑应用程序的灵活性.
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