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Catalytic constant (K cat ) is to describe the efficiency of catalyzing reactions. The K cat  value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challenging to construct robust prediction models for this important property. Most of the existing models, including the one recently published by Nature Catalysis (Li  et al. ), are suffering from the overfitting issue. In this study, we proposed a novel protocol to construct K cat  prediction models, introducing an intermedia step to separately develop substrate and protein processors. The substrate processor leverages analyzing Simplified Molecular Input Line Entry System (SMILES) strings using a graph neural network model, attentive FP, while the protein processor abstracts protein sequence information utilizing long short-term memory architecture. This protocol not only mitigates the impact of data imbalance in the original dataset but also provides greater flexibility in customizing the general-purpose K cat  prediction model to enhance the prediction accuracy for specific enzyme classes. Our general-purpose K cat  prediction model demonstrates significantly enhanced stability and slightly better accuracy (R 2  value of 0.54 versus 0.50) in comparison with Li  et al.’s  model using the same dataset. Additionally, our modeling protocol enables personalization of fine-tuning the general-purpose K cat  model for specific enzyme categories through focused learning. Using Cytochrome P450 (CYP450) enzymes as a case study, we achieved the best R 2  value of 0.64 for the focused model. The high-quality performance and expandability of the model guarantee its broad applications in enzyme engineering and drug research &amp; development.

briefings in bioinformatics

NNKcat: deep neural network to predict catalytic constants (K cat ) by integrating protein sequence and substrate structure with enhanced data imbalance handling