A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.
A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.
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DOI：
10.1038/s41598-018-35033-y
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发表时间：
2018-11-13
影响因子：
4.6
通讯作者：
Reetz MT
中科院分区：
文献类型：
--
作者：
Cadet F;Fontaine N;Li G;Sanchis J;Ng Fuk Chong M;Pandjaitan R;Vetrivel I;Offmann B;Reetz MT
定向进化是合成生物学和生物技术领域的一项重要研究活动。许多报告描述了应用繁琐的突变/筛选循环来改进蛋白质。最近，基于知识的方法促进了蛋白质特性的预测和改良突变体的鉴定。然而，上位现象构成了一个障碍，可能会损害蛋白质工程的预测。我们提出了一种基于数字信号处理、结合湿实验室实验和计算蛋白质设计的创新序列活性关系（innov’SAR）方法。在我们的机器学习方法中，开发了一个预测模型来查找当 n 个单点突变排列（2n 个组合）时蛋白质的最终特性。我们方法的独创性在于，构建模型只需要序列信息和在湿实验室中测量的突变体的适应性。我们举例说明了该方法在提高黑曲霉环氧化物水解酶对映选择性的情况下的应用。通过实验评估了该酶的 n = 9 个单点突变体的对映选择性，并将其用作学习数据集来构建模型。基于9个单点突变（29）的组合，预测了这512个变体的对映选择性，并通过实验检查了候选者：确实发现了具有更高对映选择性的更好突变体。
Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.
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影响因子：
3.8
作者：
de Trad, CH;Fang, Q;Cosic, I
通讯作者：
Cosic, I
影响因子：
46.9
作者：
Fox, Richard J.;Davis, S. Christopher;Huisman, Gjalt W.
通讯作者：
Huisman, Gjalt W.
影响因子：
8
作者：
Althoff, Eric A.;Wang, Ling;Jiang, Lin;Giger, Lars;Lassila, Jonathan K.;Wang, Zhizhi;Smith, Matthew;Hari, Sanjay;Kast, Peter;Herschlag, Daniel;Hilvert, Donald;Baker, David
通讯作者：
Baker, David
DOI：
10.1186/1753-4631-1-7
发表时间：
2007-07-19
期刊：
Nonlinear biomedical physics
影响因子：
--
作者：
Cosic, Irena;Pirogova, Elena
通讯作者：
Pirogova, Elena
影响因子：
5.6
作者：
Barley, Mark H.;Turner, Nicholas J.;Goodacre, Royston
通讯作者：
Goodacre, Royston