Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
PLoS Comput Biol. 2024 Nov 4;20(11):e1012576. doi: 10.1371/journal.pcbi.1012576. eCollection 2024 Nov.
The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications.
越来越多的实验可提供酶转换数测量值，深度学习模型也可提供转换数预测值，这促使人们在精确代谢工程中使用这些酶参数。然而，目前还没有一种计算方法可以预测依赖于转换数修饰的代谢工程策略。此外，是否仅通过改变宿主转录调控机制来改变转换数就足以提高目标化学品的产量，目前也尚不清楚。在这里，我们提出了一种基于约束的建模方法，称为克服动力学障碍（OKO），该方法使用受酶约束的代谢模型来预测在不影响指定细胞生长的情况下，增加给定化学品产量的计算策略。我们证明，将 OKO 应用于大肠杆菌和酿酒酵母的酶约束代谢模型，可产生至少将 40 多种化合物的产量提高一倍的策略，而对生长的影响很小。有趣的是，我们表明，目标化合物的过度生产并不一定只需要增加转换数的值。最后，我们证明，对 OKO 的改进，也允许操纵酶丰度，可促进在精确代谢工程策略设计中使用可用的酶周转率编目和深度学习模型。我们的研究结果扩展了基因组规模代谢模型在鉴定蛋白质工程目标方面的应用，使其能够直接用于生成各种生物技术应用的创新代谢工程设计。
