机器学习识别促进酶催化的化学特征
Brian M Bonk1,2, James W Weis2,3,4, Bruce Tidor1,2,3,4
1Department of Biological Engineering , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.
Journal of the American Chemical Society
|February 15, 2019
概括
研究人员发现了特定的酶构造, 在酸还原异构酶 (KARI) 中的这一发现为酶工程提供了一种促进工业应用的新策略.
科学领域:
- 酶催化和工程
- 计算生物物理学
- 生物化学
背景情况:
- 酶结构-动力学-功能关系尚未完全理解.
- 目前的酶工程能力不足以满足工业需求.
研究的目的:
- 研究导致酸还原异构酶 (KARI) 催化的结构和动力因素.
- 确定增强反应性的酶基质复合构造.
- 开发酶反应动力学的预测模型.
主要方法:
- 使用过渡接口采样的计算统计力学.
- 机器学习用于特征选择和预测建模.
- 对形态和动态描述符的分析.
主要成果:
- 单独的符合性描述器可以预测反应性,准确度高于85% (AUC为90%).
- 关键描述因素包括基质构成,键极化和金属协调.
- 将轨迹限制在特定的形状区域可以显著增加计算反应性.
结论:
- 在酶基质结构空间内存在促进反应性的区域.
- 为确定和验证这些区域而开发的方法.
- 通过酶改造来填充这些区域可能会带来大幅度的增速.
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