A Stereochemistry-Aware Vector–Scalar Framework for Enzyme–Substrate Specificity Prediction

Enzyme specificity determines which substrates are selectively recognized and catalyzed, but existing computational methods often provide limited modeling of pair-specific three-dimensional residue–substrate interactions. Here, we develop StereoVSE, a stereochemistry-aware Vector–Scalar framework for enzyme–substrate specificity prediction and candidate residue prioritization. StereoVSE integrates protein sequence representations, molecular features, and enzyme–substrate structural configurations through a Vector–Scalar graph neural network and Bidirectional Residue–Atom Interaction Alignment, enabling substrate-conditioned residue–atom interaction learning. Experiments on halogenase and glycosyltransferase benchmarks demonstrate improved specificity prediction performance over existing methods. Furthermore, StereoVSE generates substrate-conditioned residue–atom attribution signals that complement sequence conservation analysis and enable candidate residue prioritization.