Enhancing kcat prediction through residue-aware attention mechanism and pre-trained representations

The turnover number (kcat) is a key parameter in enzyme kinetics that quantifies catalytic efficiency and underpins mechanistic understanding of enzyme activity. However, existing computational approaches for kcat prediction often suffer from limited accuracy and generalization, largely because most models rely only on substrate information and neglect the role of the full reaction context. Although TurNuP partially addresses this limitation by encoding entire reactions using binary fingerprints, its two-stage design separates enzyme and reaction modeling, limiting both predictive performance and interpretability. Here, we present PMAK, a deep learning framework that integrates pre-trained representations of enzyme sequences and reaction SMILES with a residue-aware attention mechanism. By jointly modeling enzymes and reactions, PMAK captures their interactions and highlights key residues contributing to catalytic activity. Comprehensive evaluations demonstrate that PMAK consistently outperforms existing methods, achieving average R2 improvements of approximately 16.9% and 10.0% over TurNuP under new-reaction and new-enzyme settings, respectively, in five-fold cross-validation. Beyond improved accuracy, PMAK provides interpretable insights into residues associated with enzyme catalysis, offering a robust and informative tool for enzyme kinetic prediction and related applications. PMAK integrates pre-trained enzyme and reaction representations with residue-aware attention to improve kcat prediction, demonstrating robust generalization under warm- and cold-start settings and highlighting the value of full reaction context for unseen enzymes.