Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Background: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study.

Results: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based k data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct k collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted k values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila.

Conclusions: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based k data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.