Functional Locality-Aligned Learning Reveals Structure-Function Causality in Enzyme Kinetics

Abstract

Accurate estimation of enzyme kinetic parameters is essential for enzyme engineering and industrial biocatalysis, yet their experimental measurement remains labor-intensive and costly. Although machine learning offers an efficient alternative, existing methods still struggle to generalize to unseen enzymes and substrates. In particular, current three-dimensional (3D) structure-aware approaches rely on whole-enzyme 3D geometric structures and neglect substrate 3D geometry, often yielding limited or even degraded performance. We identify a fundamental limitation underlying these methods: a mismatch between the structural representation learning scale and the functional locality scale, which weakens structure-function causality in enzyme kinetics. To address this issue, we introduce EnzymePlex, a functional locality-aligned framework that aligns inductive biases with the localized structural determinants of enzyme function by prioritizing catalytic pockets, integrating substrate 3D geometry, and modeling nuanced enzyme-substrate interplay under the guidance of pocket-level structural priors. EnzymePlex achieves state-of-the-art performance across multiple benchmarks and substantially improves generalization under stringent out-of-distribution evaluations. Beyond predictive accuracy, EnzymePlex learns mechanistically aligned representations, with attention enriched at catalytic pocket residues and substrate reaction centers despite receiving no explicit super-vision for either. Moreover, when applied to recently reported wet-lab data, EnzymePlex effectively prioritizes high-activity enzyme variants and identifies potent inhibitors, highlighting its potential to accelerate enzyme engineering and drug discovery.