Accurate enzyme specificity constant prediction with iESC

Enzyme specificity constants (ESC) are critical quantitative metrics of enzyme properties, especially the Michaelis constant (K_m) and turnover number (k_cat). However, traditional methods for measuring K_m and k_cat are laborious and time-consuming. Here, we introduce iESC, a deep learning model that accurately predicts these parameters based solely on enzyme sequences and substrate structures. iESC was developed using a comprehensive dataset of 41,907 enzyme-substrate kinetic parameters compiled from existing databases and reports. Rigorous data preprocessing ensured independence and accuracy. By integrating multiple advanced feature extraction and deep learning techniques, iESC achieved coefficient of determination (R²) values of 0.63, 0.60, and 0.62 for K_m, k_cat, and k_cat/K_m, respectively. Benchmark tests demonstrated that iESC significantly outperformed existing state-of-the-art models, with higher R² values and lower root mean squared error (RMSE) and mean absolute error (MAE) on various datasets. We further validated iESC's outstanding applicability in the high-throughput screening (HTS) and deep mutational scanning (DMS) technologies of enzymes.