KcatNet: A Geometric Deep Learning Framework for Genome-Wide Prediction of Enzyme Catalytic Efficiency

Enzyme turnover numbers ( K _cat ) are fundamental kinetic constants that quantify enzymatic efficiency. Systematic studies of K _cat are essential for characterizing the mechanisms underlying proteomic composition and cellular metabolism. However, experimental measurements of K _cat remain limited and prone to noise. To address this, we present KcatNet, a geometric deep learning model designed for high- throughput prediction of K _cat in metabolic enzymes across all organisms, leveraging paired enzyme sequence and substrate representations. KcatNet consistently outperforms existing predictors, particularly for enzymes with high catalytic efficiency, and demonstrates strong generalization to enzymes that are dissimilar to those in the training set. The model generates interpretable importance scores for enzyme residues involved in catalysis, thereby enabling a quantitative assessment of their contributions to catalytic activity. Furthermore, KcatNet uncovers structural mechanisms and interaction patterns within enzyme-substrate complexes, providing insights into architectural principles that are inaccessible with existing methods. We applied KcatNet to genome-scale K _cat prediction across diverse yeast species, improving proteome allocation predictions by integrating its outputs into metabolic models. Experimental validation further confirmed the model’s ability to identify enzyme mutants with enhanced activity. By bridging the gap between sequence, structure, and function, KcatNet establishes a robust foundation for advancing understanding of molecular-level mechanisms and accelerating enzyme engineering efforts