SAEKP: A Site Attention Enzyme Kinetics Prediction Method to Facilitate Enzyme Engineering

Abstract While the determination of enzyme kinetic parameters traditionally relies on prolonged and costly experimental methods, machine learning models have recently demonstrated promise as predictive tools. These models still have limitations, such as limited training data and insufficient sensitivity to mutations at important sites. Here, we introduce SAEKP (Site Attention Enzyme Kinetics Prediction Method), which incorporates representations of enzyme sequence, substrate, and an additional weighted sequence representation that highlights functionally important sites. Trained on wild-type and mutant enzyme data from BRENDA and SABIO-RK, SAEKP outperforms existing models in predicting enzyme kinetic parameters, including turnover number (kcat), Michaelis constant (Km), and inhibition constant (Ki). The selected multimodal protein and substrate representation framework provides strong overall performance, while the site attention module further improves performance in predicting mutations at or near important sites through prior-knowledge-guided residue reweighting. SAEKP offers a robust and practical tool for predicting enzyme kinetic parameters, facilitating enzyme engineering and subsequent industrial applications.