Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions

New enzyme functions exist within
the increasing number of unannotated
protein sequences. Novel enzyme discovery is necessary to expand the
pathways that can be accessed by metabolic engineering for the biosynthesis
of functional compounds. Accordingly, various machine learning models
have been developed to predict enzymatic reactions. However, the ability
to predict unknown reactions that are not included in the training
data has not been clarified. In order to cover uncertain and unknown
reactions, a wider range of reaction types must be demonstrated by
the models. Here, we establish 16 expanded enzymatic reaction prediction
models developed using various machine learning algorithms, including
deep neural network. Improvements in prediction performances over
that of our previous study indicate that the updated methods are more
effective for the prediction of enzymatic reactions. Overall, the
deep neural network model trained with combined substrate–enzyme–product
information exhibits the highest prediction accuracy with Macro F1 scores up to 0.966 and with robust prediction of unknown
enzymatic reactions that are not included in the training data. This
model can predict more extensive enzymatic reactions in comparison
to previously reported models. This study will facilitate the discovery
of new enzymes for the production of useful substances.