基于结构的CRISPR/Cas9蛋白质分类:一种机器学习方法来阐明Cas9带
bioRxiv : the preprint server for biology
|April 28, 2025
概括
我们开发了一种机器学习方法,用于在CRISPR/Cas9基因编辑中映射全性网络. 这确定了关键的残留物和一个"静电谷",以设计更具体的Cas9变体.
科学领域:
- 生物化学 生物化学
- 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:
- 克里斯普尔/卡斯9基因编辑依赖于全调节来获得特异性和稳定性.
- 了解这些机制对于开发精确的Cas9变体具有更少的非目标效应至关重要.
研究的目的:
- 引入一种基于结构的新型机器学习 (ML) 方法,用于在Cas9.9中识别远程全网络.
- 在Streptococcus pyogenes Cas9 (SpCas9) 中系统地绘制和完善这些网络,以了解其稳定性和特异性.
主要方法:
- 在所有可用的Cas9结构上训练了一个ML模型,以分析Cas9结构景观.
- 应用了两阶段的SHAP特征选择策略,使用Cα-Cα间残留距离来识别关键的氨酸-氨酸 (Lys-Arg) 残留对.
- 利用分子动力学 (MD) 模拟和突变分析来研究已识别的全osteric 网络和静电谷.
主要成果:
- 确定了28个关键的lys-arg残留对,它们调解了spcas9的域间通信,稳定性和特异性.
- 在Lys-Arg对中发现了一种具有明显稳定行为的等级性全网络.
- 揭示了对SpCas9结构完整性至关重要的静电谷,突变破坏了其DNA结合的形状和影响特异性的稳定性.
结论:
- 开发了一个新的ML框架,整合了结构分析和MD模拟,以了解Cas9异构.
- 确定了关键的全残留物和静电谷,为设计高保真Cas9变体提供了合理的策略.
- 该方法适用于其他酶,以了解全调节和蛋白质设计.
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