对于高效和可控的RNA寡核酸合成,聚聚酶的计算演化
Lixiang Yang1, Yi He2, Fuyan Cao2
1MGI Tech, Shenzhen 518083, China.
Nucleic acids research
|January 28, 2026
概括
我们使用人工智能和机器学习设计了多聚合酶 (PUP) 变体,以改进RNA合成. 新的PUPdel2酶显示了对修饰核酸的增强稳定性和催化效率.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 模板独立的聚合酶,如聚U聚合酶 (PUP),对于酶性RNA合成至关重要.
- 目前的PUP在有效地结合改性核酸方面存在局限性.
研究的目的:
- 通过使用集成的人工智能驱动的工作流程,设计具有增强活动,稳定性和修改核酸结合的新型PUP变体.
- 为了克服模板独立RNA合成的局限性.
主要方法:
- 用于机械分析的高斯加速分子动力学 (GaMD).
- 机器学习 (ML) 和生成人工智能 (AI) 用于变种选和设计.
- 蛋白质语言模型 (ESM1v,ESM3) 和Rosetta用于突变预测和稳定性分析.
主要成果:
- 开发了PUPdel,这是一个截断的变体,可以通过3'-终端阻断核酸进行受控合成.
- 通过代的ML模型实现了功能活跃变体的高命中率 (47.78%至63%).
- 具有16个突变的工程PUPdel2,显示3.4°C更高的热稳定性,3.7倍更好的表达,以及3'-O-基-UTP的5.4倍更高的催化效率.
结论:
- 一个集成GaMD,ML和生成AI的AI驱动的工作流程有效地导航酶工程的序列空间.
- 设计的PUPdel2变种为生物技术应用提供了卓越的性能,包括RNA疗法.
- 突变通过静电和动态机制增强了酶的灵活性和基质结合.
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