在Cas9和Cas12蛋白中识别家族特征:使用完整蛋白特征光谱的机器学习方法
Sita Sirisha Madugula1, Pranav Pujar2, Bharani Nammi2
1Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States.
Journal of chemical information and modeling
|June 5, 2024
概括
这项研究使用机器学习确定了独特的蛋白质特征,区分了Cas9和Cas12基因编辑蛋白与非Cas蛋白的区别. 这些已识别的特征可以指导对遗传疾病进行改进的CRISPR-Cas系统的开发.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 遗传学 是一个遗传学.
背景情况:
- 克里斯普尔-卡斯技术为遗传疾病提供基因编辑,但卡斯蛋白具有尺寸和非目标效应等局限性.
- 了解Cas蛋白家族的特征对于开发改进的基因编辑工具至关重要.
研究的目的:
- 为了阐明Cas9和Cas12家族独特的蛋白质特征.
- 使用机器学习识别Cas9,Cas12和非Cas蛋白之间的区分特征.
主要方法:
- 随机森林 (RF) 二元和多类分类器使用13,494个蛋白质特征构建.
- 模型被训练来区分Cas12和Cas9与非Cas蛋白,并区分Cas9,Cas12和非Cas蛋白.
- 对测试和独立数据集进行了严格的评估.
主要成果:
- 在独立的数据集上,二进制模型实现了高精度 (Cas12的92%和Cas9的95%).
- 多类分类器获得了F1得分,约为0.98.
- 确定的主要区分特征包括Cas12的准序列顺序描述符和Cas9.9的氨基酸组成/三组成.
结论:
- 在Cas9中,特定的三 (PWN,PYY,HHA,DHI) 与DNA分裂和特异性有关.
- 鉴定的Cas9和Cas12家族特征为设计增强的基因编辑系统提供了洞察力.
- 这些发现可以指导结构修改,以提高Cas蛋白编辑能力.
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