Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Abstract

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (predictor.openkinetics.org), an open-source platform integrating thirteen kinetic parameter prediction methods accessible via one interface. The platform optionally reports similarity between query proteins and the training data of each method to provide context. A shared framework for featurisation and prediction keeps it extensible, and independent parties, including original authors, contributed many methods. A paired data portal (data.openkinetics.org) exposes a curated kinetic dataset with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.