利用蛋白质语言模型进行交叉变异CRISPR/Cas9sgRNA活动预测
Yalin Hou1, Yiming Li1, Ruiqing Zheng1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|July 2, 2025
概括
新的深度学习模型PLM-CRISPR准确地预测了不同Cas9变体的单导向RNA活动. 这种方法提高了CRISPR/Cas9基因编辑,通过利用蛋白质语言模型来改善跨变体预测,即使数据有限.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 分子生物学分子生物学
背景情况:
- 准确预测单导向RNA (sgRNA) 活性对于有效的CRISPR/Cas9基因编辑至关重要.
- 目前的预测模型通常仅限于特定的Cas9变体,阻碍了更广泛的适用性和可扩展性.
- 发现新的Cas9变种需要适应性预测工具.
研究的目的:
- 开发一种新的深度学习模型,PLM-CRISPR,用于准确的跨变体sgRNA活动预测.
- 为了利用蛋白质语言模型来捕获Cas9变体表征.
- 提高sgRNA活动预测模型的概括性和可扩展性.
主要方法:
- 利用蛋白质语言模型提取Cas9变体表示.
- 为sgRNA和蛋白质序列开发了定制的特征提取模块.
- 实施了跨变体培训战略和动态特征融合机制.
- 在三个真实世界的场景中,在七个Cas9变体中验证了模型.
主要成果:
- 在多种Cas9变异中,PLM-CRISPR在sgRNA活动预测方面显著优于现有的方法.
- 在数据稀缺的情况下表现出卓越的表现,包括新型变体.
- 通过动机分析,通过不同的Cas9变异精确识别了高活性sgRNA序列模式.
- 与传统和其他深度学习模型相比,证实了有效性.
结论:
- 对于sgRNA活动预测,PLM-CRISPR提供了一个强大的,可扩展和可通用的解决方案.
- 该模型提高了使用CRISPR/Cas9技术进行基因组修改的效率和准确性.
- 蛋白质语言模型对于基因编辑应用中的交叉变异预测是有效的.
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