Construction and Analysis of an Enzyme-Constrained Metabolic Model of <i>Corynebacterium glutamicum</i>.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for <i>Corynebacterium glutamicum</i> reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of <i>C. glutamicum</i> (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of <i>C. glutamicum</i>, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.