Scale-Invariant Dissipation Underlies Enzyme Catalytic Performance.

IF 1.9 4区 生物学 Q2 BIOLOGY
Davor Juretić, Branka Bruvo Mađarić
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引用次数: 0

Abstract

The role of energy dissipation in the evolution of living systems remains a subject of ongoing debate. Here, we quantify the dissipation associated with enzyme catalysis using minimalistic models of enzyme kinetics and a complete set of microscopic rate constants. We identify a power-law proportionality between total dissipated energy and key kinetic parameters- specifically, the catalytic constant and the specificity constant. These scale-invariant relationships hold across enzyme classes, biological domains, and natural or engineered enzymes. Consistent with Jensen's hypothesis, specialized enzymes display greater catalytic efficiency and higher dissipation. Yet, the wide range of observed efficiencies and dissipation values suggests that scale-independent organizational principles govern enzyme catalysis. Our findings indicate that biological evolution has not merely tolerated dissipation but has actively harnessed and regulated it within constraints imposed by functional and environmental demands. The scale-invariant perspective provides a unifying view of physical (dissipative) and biological (adaptive) evolutionary processes in the emergence of enzymatic function.

尺度不变耗散是酶催化性能的基础。
能量耗散在生命系统进化中的作用仍然是一个持续争论的主题。在这里,我们使用酶动力学的极简模型和一套完整的微观速率常数来量化与酶催化相关的耗散。我们确定了总耗散能量和关键动力学参数之间的幂律比例-特别是催化常数和特异性常数。这些尺度不变的关系适用于酶类、生物领域和天然或工程酶。与詹森的假设一致,特化酶表现出更高的催化效率和更高的耗散。然而,广泛观察到的效率和耗散值表明,规模无关的组织原则支配着酶催化。我们的研究结果表明,生物进化不仅容忍耗散,而且在功能和环境需求的约束下积极地利用和调节耗散。尺度不变的观点提供了一个统一的观点,物理(耗散)和生物(适应)进化过程中出现的酶的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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