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机器学习揭示了细菌中的SaCas9-PAM相互作用的序列和甲基化决定因素
Dalton T Ham1, Tyler S Browne1, Claire Q Zhang1
1Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London ON N6A 5C1, Canada.
Nucleic acids research
|January 15, 2026
概括
机器学习通过分析DNA序列来预测金黄色葡萄球菌Cas9 (SaCas9) 活动. SaCas9的活性因其标序列内的腺甲基化而降低,这表明了进化防御机制.
科学领域:
- 微生物学 微生物学
- 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:
- Cas9核酶,就像金黄色葡萄球菌Cas9 (SaCas9),是细菌对外来DNA的防御系统.
- 由单导向RNA (sgRNA) 引导的SaCas9具有作为抗菌和基因组编辑工具的潜力.
- 了解SaCas9目标DNA相互作用对于优化其细菌应用至关重要.
研究的目的:
- 开发一种机器学习模型,用于预测细菌中的SaCas9活性.
- 为了识别影响SaCas9活动的序列特征,特别是原体空间子相邻动机 (PAM) 的下游特征.
- 为了研究DNA腺甲基化对SaCas9功能的影响.
主要方法:
- 在细菌系统中生成大规模的SaCas9/sgRNA活动数据集.
- 训练了一个机器学习模型 (crispr macHine trAnsfer Learning) 来预测SaCas9的活动.
- 在缺乏DNA腺甲基转移酶 (DAM) 的大肠杆菌中进行了等离子体裂解试验,以评估甲基化效应.
主要成果:
- 通过包括NNGRRN PAM旁边的序列,提高了预测模型的性能,T丰富的二核化物在 [+1] 和 [+2] 位置增强活性.
- 在具有5'-NNGGAT[C]-3'PAM序列的部位,SaCas9的活性显著降低 (约为10倍).
- 在PAM序列内GATC基因的腺甲基化被证明可以抑制SaCas9的活性,其去除增强,其引入减少裂变.
结论:
- 机器学习可以有效地识别Cas9核酶活动的关键生物决定因素.
- DNA腺甲基化直接抑制了SaCas9的功能,这表明了自我/非自我歧视或抵消外来DNA的机制.
- 这些发现为设计更有效的基于SaCas9的基因组编辑和抗菌战略提供了洞察力.
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