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A unifying kinetic framework for modeling oxidoreductase-catalyzed reactions

机译:用于模拟氧化还原酶催化反应的统一动力学框架

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Motivation: Oxidoreductases are a fundamental class of enzymes responsible for the catalysis of oxidation-reduction reactions, crucial in most bioenergetic metabolic pathways. From their common root in the ancient prebiotic environment, oxidoreductases have evolved into diverse and elaborate protein structures with specific kinetic properties and mechanisms adapted to their individual functional roles and environmental conditions. While accurate kinetic modeling of oxidoreductases is thus important,current models suffer from limitations to the steady-state domain, lack empirical validation or are too specialized to a single system or set of conditions. Results: To address these limitations, we introduce a novel unifying modeling framework for kinetic descriptions of oxidoreductases. The framework is based on a set of seven elementary reactions that (i) form the basis for 69 pairs of enzyme state transitions for encoding various specific microscopic intra-enzyme reaction networks (micro-models), and (ii) lead to various specific macroscopic steady-state kinetic equations (macro-models) via thermodynamic assumptions. Thus, a synergistic bridge between the micro and macro kinetics can be achieved, enabling us to extract unitary rate constants, simulate reaction variance and validate the micro-models using steady-state empirical data. To help facilitate the application of this framework, we make available RedoxMech: a Mathematica software package that automates the generation and customization of micro-models. Availability: The Mathematica source code for RedoxMech, the documentation and the experimental datasets are all available from: http://www.igb.uci.edu/tools/sb/metabolic-modeling.
机译:动机:氧化还原酶是负责催化氧化还原反应的基本酶类,在大多数生物能代谢途径中至关重要。氧化还原酶从它们在古代益生元环境中的共同根源演变成具有特定动力学特性和机制的多样且精细的蛋白质结构,其机制适合于其各自的功能作用和环境条件。因此,准确的氧化还原动力学建模非常重要,但当前的模型受稳态域的限制,缺乏经验验证或过于专门于单个系统或一组条件。结果:为了解决这些限制,我们为氧化还原酶的动力学描述引入了一个新颖的统一建模框架。该框架基于一组七个基本反应,这些反应(i)形成69对酶状态转换的基础,用于编码各种特定的微观内酶反应网络(微观模型),以及(ii)导致各种特定的宏观通过热力学假设得出的稳态动力学方程(宏模型)。因此,可以实现微观动力学和宏观动力学之间的协同作用,从而使我们能够提取稳态速率常数,模拟反应方差并使用稳态经验数据验证微观模型。为了帮助促进该框架的应用,我们提供了RedoxMech:Mathematica软件包,该软件包可以自动生成和定制微模型。可用性:RedoxMech的Mathematica源代码,文档和实验数据集均可从以下网站获得:http://www.igb.uci.edu/tools/sb/metabolic-modeling。

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