Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Xue Yang; Zhitao Mao; Jianfeng Huang; Ruoyu Wang; Huaming Dong; Yanfei Zhang; Hongwu Ma

doi:10.1016/j.synbio.2023.09.002

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Synth Syst Biotechnol. 2023 Sep 12;8(4):597-605. doi: 10.1016/j.synbio.2023.09.002. eCollection 2023 Dec.

Authors

Xue Yang^{1

2}, Zhitao Mao^{1

2}, Jianfeng Huang^{1

2}, Ruoyu Wang^{1

2}, Huaming Dong^{1

2

3}, Yanfei Zhang^{1

2}, Hongwu Ma^{1

2}

Affiliations

¹ Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
² National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China.
³ School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan, 430205, China.

Abstract

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

Keywords: Compartmentalization; Enzymatic and thermodynamic constraints; Enzyme complexes; Genome-scale metabolic network models (GEMs); Multifunctional enzymes; Thermodynamic driving force (MDF).