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Updated: May 12, 2025

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通过深度学习预测不同细胞环境中的腺基编辑效率

Lucas Kissling¹, Amina Mollaysa², Sharan Janjuha¹

¹Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.

|May 9, 2025

概括

腺基编辑器 (ABEs) 可以纠正致病突变. 这项研究验证了ABE in vivo,并引入了BEDICT2.0,这是一个深度学习模型,预测编辑效率以改善治疗开发.

科学领域:

基因编辑技术基因编辑技术
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:

腺基编辑器 (ABEs) 促进了A•T到G•C的转换.
基准编辑效率的预测模型受到体外数据的限制.
原始细胞的体内预测能力仍然不确定.

研究的目的:

在体外和体内评估腺基编辑效率.
为基础编辑结果开发一个预测计算模型.
评估ABE在纠正病原性突变方面的潜力.

主要方法:

使用SpRY-ABEmax和SpRY-ABE8e进行基础编辑屏幕.
针对细胞系和小鼠肝脏模型中的2,195种致病突变.
开发了BEDICT2.0,这是一个用于预测编辑效率的深度学习模型.

主要成果:

观察到体外和体内基调编辑数据集之间的强烈相关性 (斯皮尔曼R = 0.83-0.92).
BEDICT2.0准确预测了细胞系 (R = 0.60-0.94) 和肝脏 (R = 0.62-0.81) 中的腺基编辑效率.
展示了高目标编辑与最小的旁观者效应.

结论:

腺基编辑显示了纠正众多病原性突变的巨大潜力.
BEDICT2.0是一个强大的计算工具,用于优化sgRNA-ABE组合.
这些发现支持使用ABE用于体外和体内治疗应用.

关键词:

通过CRISPR-Cas9进行基因组编辑.基因组学就是基因组学.在生物体内,在生物体内.机器学习是机器学习.鼠标鼠标是一个鼠标.

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