相关实验视频
Updated: May 10, 2025
07:56
Genome Editing in Mammalian Cell Lines using CRISPR-Cas
Published on: April 11, 2019
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通过可扩展工程和机器学习定制CRISPR-Cas9 PAM变体
Rachel A Silverstein1,2,3, Nahye Kim2,3,4, Ann-Sophie Kroell2,3,5
1PhD Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.
Nature
|April 22, 2025
概括
研究人员开发了一种机器学习工具,即PAM机器学习算法 (PAMmla),用于设计定制的Cas9酶以进行精确的基因组编辑. 这种方法通过减少非目标编辑来创建更安全,更有效的基因编辑工具.
科学领域:
- 生物技术
- 分子生物学
- 基因组学
背景情况:
- 虽然CRISPR-Cas9技术可以进行多种基因组编辑,但一般性酶可能会产生异常编辑.
- 为特定的基因组编辑应用设计和表征蛋白质是耗时的.
- 开发更安全,更有效的基因组编辑工具至关重要.
研究的目的:
- 使用机器学习和高通量蛋白质工程来开发可扩展的Cas9酶重编程方法.
- 创建针对特定基因组目标的定制 Cas9 编辑器, 提高安全性和有效性.
- 识别具有明显的原体空间相邻基因 (PAM) 的新 Cas9 变体.
主要方法:
- 结合高通量蛋白质工程与机器学习来获得定制的Cas9编辑器.
- 利用结构功能告知和突变和细菌选择来产生工程化SpCas9酶.
- 训练了一个神经网络 (PAM机器学习算法 - PAMmla) 以基于氨基酸序列来预测PAM特异性.
主要成果:
- 产生了近1000个工程SpCas9酶,并描述了它们的PAM需求.
- 确定了有效和特定的Cas9酶,其性能优于人类细胞中的现有变体.
- 已证明减少了非目标编辑,并允许使用in silico-directed evolution选择性向.
结论:
- PAMmla集成机器学习和蛋白质工程,以创建具有独特PAM特性的SpCas9酶的多样性目录.
- 这种方法有助于从通用化转向定制的Cas9变种,从而实现更安全,更有效的基因组编辑.
- 开发的方法可以为精确的治疗应用提供用户指导的Cas9酶设计.
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