EnzyKR: A Chirality-Aware Deep Learning Model for Predicting the Outcomes of the Hydrolase-Catalyzed Kinetic Resolution

Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict the favorable enzyme scaffolds for separating 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 an enantiomer-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 sequence and substrate SMILES strings, EnzyKR was trained using 204 enantiomer-hydrolase complexes, which were constructed by docking based on the enzyme and substrate structures curated from IntEnzyDB. EnzyKR was tested using a held-out dataset of 20 complexes on the task of active free energy prediction. 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 in its active free energy prediction 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 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 as a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.