An Improved Systematic Method for Constructing Enzyme-Constrained Genome-Scale Metabolic Models Using a Protein-Chemical Transformer

Enzyme-constrained genome-scale metabolic models (ecGEMs) have improved Flux Balance Analysis (FBA) by incorporating enzyme turnover numbers ( k cat s). Since in-vivo k cat data is costly to obtain and therefore scarce, we present a novel multi-modal transformer-based approach with cross-attention to predict k cat values for Escherichia coli using enzyme amino acid sequences and SMILES annotations of reaction substrates. For heteromeric enzymes, we evaluate multiple subunit k cat aggregation strategies. We benchmark ecGEMs constructed with these strategies against current state-of-the-art models using experimental growth rates, 13 C fluxes, and enzyme abundances, and prior to any calibration outperform or match existing methods. We also devise a new calibration method using flux control coefficients (derivatives of log flux with respect to log k cat ), which we show to be identical to enzyme cost at the FBA optimum. Using these coefficients, we identify 8 key k cat values to recalibrate using experimental data, subsequently achieving superior performance to the current state-of-the-art with 81% fewer calibrations.