EnzymeTuning improves enzyme-constrained metabolic modeling and proteome abundance prediction through deep learning
Year: 2026
Type: article
Abstract: The accuracy of enzyme kinetic parameters, particularly enzyme turnover numbers (kcat), is critical for the predictive performance of enzyme-constrained genome-scale metabolic models. However, currently available kinetic datasets remain sparse and often fail to capture in vivo enzyme behavior, thereby limiting model accuracy. To address these limitations, we develop EnzymeTuning, a generative adversarial network-based framework for global kcat optimization. By further incorporating literature-derived protein degradation constants, we infer protein synthesis rates and systematically assess their impact on model performance. Here, we show that EnzymeTuning substantially improves prediction accuracy and expands proteome-level coverage across diverse organisms, including Saccharomyces cerevisiae, Kluyveromyces lactis, Kluyveromyces marxianus, Yarrowia lipolytica, and Escherichia coli. Furthermore, EnzymeTuning reveals context-dependent enzyme usage patterns and adaptive catalytic resource allocation under diverse carbon- and nitrogen-limited chemostat conditions, underscoring the substantial potential of this framework for integrative multi-omics analyses. The accuracy of enzyme kinetic parameters is critical for the predictive performance of enzyme-constrained genome-scale metabolic models. Here the authors present EnzymeTuning, a generative adversarial network-based framework that refines enzyme parameters to improve metabolic models.
Source: Nature Communications
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: G3703027713, G5427955262, G6185739678
OpenAlex ID: W7162503303