CatRange enables robust prediction of enzyme variant kinetic regimes

Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly for mutations at catalytically essential residues. Existing models often default to wild-type predictions even when mutations cause substantial activity loss. This limits their utility for enzyme design and mechanistic inference. Here, we introduce CatRange, a machine learning framework trained on CatLog-27k, a human-in-the-loop, AI-agent curated dataset of 27,176 in vitro enzyme-substrate kinetic records. CatLog-27k was created through systematic audit and correction of BRENDA and SABIO-RK, with mutant entries manually reconciled against 2,158 source articles. CatRange reframes kinetic prediction as classification into log₁₀-spaced bins for catalytic turnover (k _cat ) and substrate affinity (K_M ), rather than exact-value regression. This formulation reflects the order-of-magnitude scale at which enzyme kinetics are commonly interpreted. It reduces sensitivity to assay variability while preserving distinctions among functional catalytic and binding states. Using joint enzyme-substrate representations and gradient-boosted classifiers, CatRange predicts kinetic ranges for wild-type and mutant enzymes across held-out, out-of-distribution, and few-shot mutation settings. The model achieves robust order-of-magnitude recovery and class-balanced discrimination. Moreover, it captures mutation-induced transitions across kinetic regimes, including activity losses caused by perturbations of annotated catalytic residues. CatRange also identifies nonenzyme sequence inputs. Together, CatLog and CatRange provide an interpretable, mutation-sensitive framework for enzyme engineering and kinetic metabolic modeling.