PMAK: Enhancing kcat Prediction through Residue-Aware Attention Mechanism and Pre-Trained Representations

The turnover number (kcat) is a critical parameter in enzyme kinetics, representing the maximum number of substrate molecules an enzyme can convert to product per unit time under optimal conditions. While kcat reflects an enzyme's catalytic efficiency and plays a central role in understanding enzyme activity, existing computational models for predicting this parameter often face significant limitations. Most models rely solely on substrate information, overlooking the integral role of the reaction itself in predicting kcat. The only model that attempts to incorporate reaction information, TurNuP, still falls short in both predictive accuracy and interpretability. To address these challenges, we propose PMAK, a deep learning model that leverages pre-trained representation learning and a residue-aware attention mechanism. By incorporating both enzyme sequences and reaction details, PMAK generates robust representations, effectively capturing the complex interplay between enzyme and reaction, and offering superior accuracy and generalization compared to existing methods. Specifically, PMAK achieved an average R2 improvement of approximately 16.9% and 10.0% over TurNup under new reaction setting and new enzyme setting in five-fold cross-validation, respectively. Besides, our approach provides valuable insights into the key features driving enzyme catalysis, making it a more interpretable solution for enzymatic reaction prediction.