机器学习辅助的工程一个P450细胞染色体的最佳生物转化素碎片的机器学习辅助工程
Artur Hermano Sampaio Dias1,2, Yuanxin Cao1, Munir S Skaf2
1Manchester Institute of Biotechnology and Department of Chemical Engineering, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. sam.devisser@manchester.ac.uk.
Physical chemistry chemical physics : PCCP
|June 17, 2024
概括
使用计算方法研究了细胞染色体P450在基质激活中的选择性. 第二个协调范围显著影响结合,接和反应结果,使得有针对性的生物催化剂设计成为可能.
科学领域:
- 生物化学和计算化学.
- 酵素学和生物催化剂学
背景情况:
- 细胞染色体P450 (P450s) 是关键的酶,参与了异生生物的生物降解.
- 菌类使用P450来激活素碎片,这些碎片是药物和香料的宝贵前体.
- 开发选择性P450生物催化剂是生物技术应用的关键.
研究的目的:
- 通过计算来研究两个P450异酶中的基质激活选择性.
- 阐明第二协调球在P450催化机制中的作用.
- 为了确定增强P450反应特异性的潜在突变.
主要方法:
- 机器学习,分子动力学模拟和密度函数理论计算.
- 对两个P450反应通道进行详细的计算研究:CYP255A和CYP199A4.
- 对基质结合,血红素接入和动力屏障高度的分析.
主要成果:
- 第二个协调球具有关键影响基质结合,定位和heme访问.
- 局部微环境通过改变激活能量障碍来调节反应动力学.
- 预测了一种选择性基因突变,用于增强CYP199A4的反应特异性.
结论:
- 第二个协调球是P450选择性的主要决定因素.
- 计算方法为P450反应机制提供了宝贵的见解.
- 有针对性的突变发生可以为特定的生物技术应用设计P450s.
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