Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
Nat Commun. 2023 May 5;14(1):2618. doi: 10.1038/s41467-023-38159-4.
Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.
解析生物体的代谢功能需要了解活细胞在遗传和环境干扰下的动态响应,而这反过来又可以从酶活性中推断出来。在这项工作中,我们根据推动酶向更高催化效率进化的压力,研究了酶的最佳操作模式。我们使用混合整数公式开发了一个框架来评估热力学力和酶状态的分布,从而深入了解酶的操作模式。我们使用这个框架来探索米氏动力学和随机顺序多底物机制。我们表明,最优的酶利用是通过依赖于反应物浓度的独特或替代操作模式来实现的。我们发现,在双分子酶反应中,在生理条件下,随机机制比任何其他有序机制都更优。我们的框架可以研究复杂酶机制的最佳催化特性。它可以进一步指导酶的定向进化,并填补酶动力学中的知识空白。