深度学习和CRISPR-Cas13d正方体的发现,以优化RNA向
Jingyi Wei1, Peter Lotfy2, Kian Faizi2
1Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Biochemistry, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA.
Cell systems
|December 13, 2023
概括
研究人员开发了一种机器学习模型,用于预测CRISPR-Cas13系统的引导RNA效率,增强转录组工程. 一种新的酶,DjCas13d,对人类细胞中的RNA向具有低毒性和高特异性.
科学领域:
- 分子生物学分子生物学
- 生物技术是生物技术.
- 基因组学就是基因组学.
背景情况:
- CRISPR-Cas13系统提供可编程的RNA向,但在指导RNA设计和细胞毒性方面面临挑战.
- 了解指导RNA效率和Cas13正方体对于推进RNA疗法和生物发现至关重要.
研究的目的:
- 使用机器学习开发CRISPR-Cas13指导RNA效率的预测模型.
- 识别和表征新的Cas13d正方体,其特异性提高,对哺乳动物转录基因组工程有所降低.
主要方法:
- 超过127,000个RfxCas13d (CasRx) 指导RNA的量化性能.
- 评估了七个机器学习模型,以构建一个指导效率预测算法.
- 识别并选了46个新的Cas13d正义词,包括DjCas13d.
主要成果:
- 开发并验证了一种深度学习模型,用于预测人类细胞类型的引导RNA效率.
- 确定了对高效率指南至关重要的序列动图和次要特征.
- DjCas13d表现出低细胞毒性和高特异性,即使对于干细胞和神经元等敏感细胞中丰富的转录.
结论:
- 这种Cas13d指导效率模型可以很好地将其推广到像DjCas13d这样的新型正义模型上.
- 结合机器学习和ortolog发现,显著提升了人类细胞中的RNA准能力.
- 这项工作为精确的转录组工程和RNA疗法开发提供了一个强大的平台.
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