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利用底物亲和力提高酶活性的热力学原理。

Thermodynamic principle to enhance enzymatic activity using the substrate affinity.

机构信息

Biofunctional Catalyst Research Team, Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.

Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki, 305-8577, Japan.

出版信息

Nat Commun. 2023 Aug 24;14(1):4860. doi: 10.1038/s41467-023-40471-y.

Abstract

Understanding how to tune enzymatic activity is important not only for biotechnological applications, but also to elucidate the basic principles guiding the design and optimization of biological systems in nature. So far, the Michaelis-Menten equation has provided a fundamental framework of enzymatic activity. However, there is still no concrete guideline on how the parameters should be optimized towards higher activity. Here, we demonstrate that tuning the Michaelis-Menten constant ([Formula: see text]) to the substrate concentration ([Formula: see text]) enhances enzymatic activity. This guideline ([Formula: see text]) was obtained mathematically by assuming that thermodynamically favorable reactions have higher rate constants, and that the total driving force is fixed. Due to the generality of these thermodynamic considerations, we propose [Formula: see text] as a general concept to enhance enzymatic activity. Our bioinformatic analysis reveals that the [Formula: see text] and in vivo substrate concentrations are consistent across a dataset of approximately 1000 enzymes, suggesting that even natural selection follows the principle [Formula: see text].

摘要

理解如何调节酶的活性不仅对于生物技术应用很重要,而且对于阐明指导自然界中生物系统设计和优化的基本原理也很重要。到目前为止,米氏方程为酶的活性提供了一个基本框架。然而,对于如何优化参数以获得更高的活性,仍然没有具体的指导方针。在这里,我们证明了将米氏常数([Formula: see text])调节到与底物浓度([Formula: see text])相匹配可以提高酶的活性。这条准则([Formula: see text])是通过假设热力学有利的反应具有更高的速率常数,并且总驱动力是固定的,从数学上得到的。由于这些热力学考虑具有普遍性,我们提出[Formula: see text]作为提高酶活性的一般概念。我们的生物信息学分析表明,[Formula: see text]和体内的底物浓度在大约 1000 种酶的数据集上是一致的,这表明即使是自然选择也遵循[Formula: see text]这一原则。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08b9/10449852/e468234c0f01/41467_2023_40471_Fig1_HTML.jpg

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