机器学习辅助的胺酶催化酶选择性预测和变体的合理设计,以改善选择性
Zi-Lin Li1,2, Shuxin Pei3, Ziying Chen3
1Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China.
Nature communications
|October 10, 2024
概括
这项研究引入了一种机器学习模型,用于预测生物催化剂对合成奇拉化合物的酶选择性. 这种方法加速了用于制药生产的高效酶的发现.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算化学计算化学
- 机器学习在化学中的应用
背景情况:
- 评估和增强生物催化剂对奇拉合成的酶选择性是耗时的.
- 机器学习已经探索了酶序-enantioselectivity,但往往忽视了基质适应性.
- 开发生物催化性能预测模型仍然是一个挑战.
研究的目的:
- 开发一种机器学习模型,用于预测对新基质的胺酶反选择性.
- 为改善生物催化剂设计建立基板适应性空间.
- 为了实现高效的合理蛋白质工程,以增强手性化合物合成.
主要方法:
- 从以前的研究中使用了240个数据集.
- 使用化学和几何描述符.
- 构建了随机森林分类模型来预测enantioselectivity.
- 为合理的蛋白质工程提出了一个启发式策略.
主要成果:
- 开发了关于氨基酶反选择性的预测模型.
- 展示了有效的蛋白质工程的启发式策略.
- 与野生类型的阿米达酶相比,在优化变体中达到53倍的E值.
- 成功合成了具有较高反体过量 (ee) 值的性化合物.
结论:
- 集成机器学习和基质描述器的数据驱动方法可以预测和改善生物催化剂的酶选择活性.
- 这种方法促进了合理的酶工程,以实现高效的合合成.
- 预计这些发现将扩大机器学习在生物催化剂研究中的应用.
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