EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.
EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.
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DOI：
10.1039/d3sc02752j
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发表时间：
2023-11-08
期刊：
影响因子：
8.4
通讯作者：
Yang, Zhongyue J.
中科院分区：
文献类型：
--
作者：
Ran, Xinchun;Jiang, Yaoyukun;Shao, Qianzhen;Yang, Zhongyue J.
水解酶催化的动力学拆分是一种成熟的生物催化过程。然而，预测用于分离外消旋底物混合物的有利的酶支架的计算工具是欠发达的。为了应对这一挑战，我们训练了一个深度学习框架EnzyKR，以自动选择水解酶进行立体选择性生物催化。EnzyKR采用分类器-回归器结构，首先识别底物-水解酶复合物的反应性结合构象，然后预测其活化自由能。基于结构的编码策略被用来描述水解酶和对映体之间的手性相互作用。与现有的蛋白质序列和底物SMILES字符串训练模型不同，EnzyKR使用204个底物水解酶复合物进行训练，这些复合物通过对接构建。使用20个复合物的保留数据集对EnzyKR进行了预测活化自由能的测试。在这项任务中，EnzyKR的Pearson相关系数（R）为0.72，斯皮尔曼等级相关系数（斯皮尔曼R）为0.72，平均绝对误差（MAE）为1.54 kcal mol−1。此外，还对28个氟乙酸脱卤酶RPA 1163、卤代醇HheC、A. mediolanus环氧化物水解酶和荧光假单胞菌酯酶。酶KR的性能进行了比较，对最近开发的动力学预测，DLKcat。EnzyKR正确预测了有利的对映体，并在28个反应中的18个中优于DLKcat，占测试案例的64%。这些结果表明，酶KR是一种新的方法预测的对映体的水解酶催化的动力学拆分反应的结果。EnzyKR旨在指导水解酶支架的鉴定，用于拆分立体选择性合成的外消旋底物混合物。
Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier–regressor architecture that first identifies the reactive binding conformer of a substrate–hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate–hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol−1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions. EnzyKR is designed to guide the identification of hydrolase scaffolds for resolving a racemic substrate mixture for stereoselective synthesis.
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