KiMChEE: Reaction Context from Co-Substrates and Environment Improves Kinetic Prediction for Uncharacterized Enzymes

A growing number of machine learning models predict enzyme kinetic parameters such as turnover number ($k_{cat}$) and Michaelis constant ($K_m$), along with their ratio ($k_{cat}/K_m$), from sequence and substrate information for enzyme engineering and metabolic studies. However, their accuracy degrades sharply on enzyme sequences that have never been characterized. We hypothesize that one reason for this failure lies in the input representation. Most existing methods describe a reaction by an enzyme sequence and a main substrate, discarding the remaining reactants and the assay conditions that together determine the kinetic parameter values. Consequently, records sharing an enzyme and a main substrate but differing in reaction context (e.g., different co-substrates and reaction conditions) map to identical inputs with conflicting labels. We therefore build a multimodal kinetic predictor, KiMChEE (Kinetic parameter prediction Model for Chemicals, Enzymes, and Environment). KiMChEE augments the conventional enzyme-and-substrate input with the reaction's other reactants, the co-substrates, and with the assay pH and temperature. It also pools residue-level embeddings with higher weight on substituted positions, keeping variants (e.g., engineered enzymes) separable from their wild types. An ablation isolates the contribution of the co-substrate input, showing that it improves predictions on records whose inputs would otherwise be indistinguishable despite conflicting labels while leaving the remaining records essentially unchanged. We compare methods on an out-of-distribution benchmark constructed from records absent from the public repositories (BRENDA and SABIO-RK) used to train the compared models, so that none of them can have seen these records during training. KiMChEE attains the highest $R^2$ on all three kinetic parameters under this benchmark, with a margin of 0.128 $R^2$ on $k_{cat}$ over the second-best model. These results support our hypothesis that the reaction context, rather than an enzyme and a single substrate alone, belongs in the input for kinetic prediction on novel enzymes.