Citation: Niu, J.; Mao, Z.; Mao, Y.; Wu, K.; Shi, Z.; Yuan, Q.; Cai, J.; Ma, H. Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum. Biomolecules 2022, 12, 1499. https:// doi.org/10.3390/biom12101499 Academic Editor: Kouichi Kuroda Received: 30 August 2022 Accepted: 14 October 2022 Published: 17 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biomolecules Article Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum Jinhui Niu 1,2,3 , Zhitao Mao 2,3, * , Yufeng Mao 2,3 , Ke Wu 2,3 , Zhenkun Shi 2,3 , Qianqian Yuan 2,3 , Jingyi Cai 2,3 and Hongwu Ma 1,2,3, * 1 School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China 2 Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China 3 National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China * Correspondence: mao_zt@tib.cas.cn (Z.M.); ma_hw@tib.cas.cn (H.M.) Abstract: 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 experimen- tally 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 Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (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 predic- tion of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering. Keywords: enzyme-constrained model; Corynebacterium glutamicum; metabolic engineering 1. Introduction Corynebacterium glutamicum is widely known as an excellent producer of amino acids [1]. Recent advances in metabolic engineering and synthetic biology have expanded the scope of chemicals that can be produced from C. glutamicum, but it remains difficult to synthesize these compounds on an industrially relevant scale [2]. Genome-scale metabolic models (GEMs) are a proven tool for the prediction of cellular behavior and the discovery of potential engineering targets [3]. Several GEMs of C. glutamicum have been developed (Figure S1), and used to guide the production of high-value compounds such as glutaric acid [4], anthocyanins [5] and L-glutamate family amino acids [6]. The most widely used model of C. glutamicum is iCW773, constructed in 2017 [7], which accurately predicts the growth of cells cultured under different conditions. Although the quality of C. glutamicum models has improved in the last decade, they mostly only consider reaction stoichiometries and do not accurately depict the real situation inside the cell [8]. For example, metabolic overflow is a phenomenon in which incomplete oxidation of glucose to ethanol or acetate occurs in microorganisms in the presence of sufficient substrate, which has been recognized for a long time and frequently occurs in microbial cultures [9]. It has been shown that Biomolecules 2022, 12, 1499. https://doi.org/10.3390/biom12101499 https://www.mdpi.com/journal/biomolecules