Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants.
Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants.
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DOI：
10.1371/journal.pone.0147596
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Siegel JB
中科院分区：
文献类型：
--
作者：
Carlin DA;Caster RW;Wang X;Betzenderfer SA;Chen CX;Duong VM;Ryklansky CV;Alpekin A;Beaumont N;Kapoor H;Kim N;Mohabbot H;Pang B;Teel R;Whithaus L;Tagkopoulos I;Siegel JB
使用计算建模算法来指导新型酶催化剂的设计是一个快速发展的领域。基于力场的方法现在已被用于工程酶的特异性和活性。然而，具有预期功能的设计突变体的比例通常小于10%。一个潜在的原因是，目前基于力场的方法是在功能的间接测量上训练的，而不是与实验确定的突变功能效应的直接相关性。我们推测，这部分是由于缺乏数据集的一个大的面板的酶的变体已经产生，纯化，和动力学特征。在这里，我们报告的kcat和KM值的100个纯化的突变体的糖苷水解酶。我们通过使用机器学习来训练一种新的算法来证明该数据集的实用性，该算法能够基于易于建模的结构特征来预测每个动力学参数。本研究中生成的数据集和分析不仅提供了对这种酶如何发挥作用的深入了解，还为改进计算酶重新设计算法提供了明确的前进道路。
The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.