机器学习引导的非黑米铁基氨酸二氧化酶的合理设计改善了其总营业额
R Hunter Wilson1, Anoop R Damodaran1, Ambika Bhagi-Damodaran1
1Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, MN, 55455.
bioRxiv : the preprint server for biology
|June 19, 2024
概括
机器学习和分子动力学加速了对选择性C-H功能化的酶工程. 这种方法有效地识别了氨酸二氧化酶 (LDO) 的有益突变,改善了酶的性能和产量.
科学领域:
- 有机化学 有机化学
- 生物催化剂是一种生物催化剂.
- 蛋白质工程是指蛋白质工程.
背景情况:
- 选择性C-H功能化是合成有机化学的一个关键挑战.
- 生物催化剂为复杂的化学转换提供了强大的解决方案.
- 酶工程传统上依赖于定向进化或理性设计,这可能是耗时的.
研究的目的:
- 开发一种低障碍的协同方法,将机器学习和分子动力学结合起来,用于生物催化剂工程.
- 理性地设计一种非血红素铁依赖的氨酸二氧化酶 (LDO) 的改进变体.
主要方法:
- 利用基于结构的机器学习算法与经典分子动力学模拟集成.
- 采用这种结合方法来下行选择突变,以合理设计LDO.
- 纯化和特征化工程LDO单个和五变异的变体.
主要成果:
- 机器学习和分子动力学的结合方法成功识别了功能性LDO突变.
- 与野生类型 (WT) 相比,理性设计的单个突变显示出高达2倍的净化产量和增强的总周转率 (TTN).
- 一种 pentamutant 变种 (LPNYI LDO) 在 TTN (218±3) 中比 WT LDO (160±2) 改善了 40%.
结论:
- 本研究提出了一种有效的策略,用于将机器学习与已建立的蛋白质工程方法进行协同.
- 开发的方法简化了有益的酶变体的发现,减少了广泛的先前查的需要.
- 工程设计的LDO变种证明了C-H功能化应用的增强催化效率.
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