Enzyme Kinetics at the Molecular Level

Dua, Arti

doi:10.1007/s12045-019-0781-9

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…This implies that the initial mean rate of product formation, i.e. the counting process alone, is sufficient to yield the steady-state enzymatic velocity V ss = d t n | t→0 , and the transient regime remains unobserved [14].…”

Section: Statistical Measuresmentioning

confidence: 99%

“…At the molecular level, however, enzymatic reactions do not proceed deterministically [7][8][9][10][11]. Fluctuations, of both quantum mechanical and thermal origin, termed "molecular noise", influence each step of a chemical reaction, such that neither the lifetime of a chemical state nor the state to which it transits can be known with certainty [12][13][14]. Further, the discrete change in the reactant numbers is comparable to the number of reacting molecules, and a description in terms of continuously varying concentrations is inadmissible [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Fluctuations, of both quantum mechanical and thermal origin, termed "molecular noise", influence each step of a chemical reaction, such that neither the lifetime of a chemical state nor the state to which it transits can be known with certainty [12][13][14]. Further, the discrete change in the reactant numbers is comparable to the number of reacting molecules, and a description in terms of continuously varying concentrations is inadmissible [12][13][14][15][16]. In the limit of large numbers of reactants, when both fluctuations and the change in reactants compared to their total number are small, a deterministic description in terms of continuously varying concentrations is recovered [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

Kumar,

Adhikari,

Dua

2020

Preprint

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The hyperbolic dependence of catalytic rate on substrate concentration is a classical result in enzyme kinetics, quantified by the celebrated Michaelis-Menten equation. The ubiquity of this relation in diverse chemical and biological contexts has recently been rationalized by a graph-theoretic analysis of deterministic reaction networks. Experiments, however, have revealed that "molecular noise" -intrinsic stochasticity at the molecular scale -leads to significant deviations from classical results and to unexpected effects like "molecular memory", i.e., the breakdown of statistical independence between turnover events. Here we show, through a new method of analysis, that memory and non-hyperbolicity have a common source in an initial, and observably long, transient peculiar to stochastic reaction networks of multiple enzymes. Networks of single enzymes do not admit such transients. The transient yields, asymptotically, to a steady-state in which memory vanishes and hyperbolicity is recovered. We propose new statistical measures, defined in terms of turnover times, to distinguish between the transient and steady states and apply these to experimental data from a landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites. Our study shows that catalysis at the molecular level with more than one enzyme always contains a non-classical regime and provides insight on how the classical limit is attained.

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Section: Statistical Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

Kumar,

Adhikari,

Dua

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…7,8 In this account, numerous theoretical and experimental techniques have been undertaken to investigate the distribution and conformational fluctuations associated with the enzymatic reactions. 4,6,7,9–12…”

Section: Introductionmentioning

confidence: 99%

“…1,2 The Classical Michaelis-Menten (MM) enzyme kinetics approximation has been extensively used to explore different aspect of the ensemble averaged Understanding the dynamics of complex in-situ enzyme-catalyzed reactions undergoing mechanical stress enzymatic kinetic scenarios. [4][5][6][7] However, at molecular level, studying the dynamics of enzymatic mechanism becomes more challenging as the spontaneous intrinsic fluctuations impart inherent stochasticity which influence the precision of the enzymatic processes. 7,8 In this account, numerous theoretical and experimental techniques have been undertaken to investigate the distribution and conformational fluctuations associated with the enzymatic reactions.…”

Section: Introductionmentioning

confidence: 99%

Understanding the dynamics of complexin-situenzyme-catalyzed reactions undergoing mechanical stress

Tikader,

Kar

2023

Preprint

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Understanding the diversity in the enzymatic mechanism have utmost importance as it can temporally control the catalytic efficiency. Recent literature suggests that by influencing mechanical properties of hybrid materials, the catalytic efficiency of the enzymatic reactions can be altered significantly. Here, taking a computational and experimental approach, we have dig out the fate of the kinetics of enzyme reaction systems exhibiting relatively complex mechanism than usual Michaelis Menten kinetics involving multiple substrate/enzyme/enzyme-substrate complex. We have developed a numerical recipe improvising stochastic reaction-diffusion approach to explore the role of mechanical stress like compression-decompression cycle (C-D) on modulating the output of enzymatic reaction. The proposed methodology can be used as a theoretical tool to understand how to enhance the catalytic activity and setup appropriate reaction conditions by efficiently using the catalytic cycles. Hence, our methodology will be crucial to identify the most effective strategy to efficiently convert substrate into product in this type of mechanoresponsive materials, which will enable future development of cost-effective biomaterials. In future, the insights gained from this work may find enormous application in drug delivery, tissue engineering, bio-sensing and bio-catalysis.

show abstract

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

Kumar

Adhikari

Dua

2021

The Journal of Chemical Physics

View full text Add to dashboard Cite

The hyperbolic dependence of catalytic rate on substrate concentration is a classical result in enzyme kinetics, quantified by the celebrated Michaelis–Menten equation. The ubiquity of this relation in diverse chemical and biological contexts has recently been rationalized by a graph-theoretic analysis of deterministic reaction networks. Experiments, however, have revealed that “molecular noise”—intrinsic stochasticity at the molecular scale—leads to significant deviations from classical results and to unexpected effects like “molecular memory,” i.e., the breakdown of statistical independence between turnover events. Here, we show, through a new method of analysis, that memory and non-hyperbolicity have a common source in an initial, and observably long, transient peculiar to stochastic reaction networks of multiple enzymes. Networks of single enzymes do not admit such transients. The transient yields, asymptotically, to a steady-state in which memory vanishes and hyperbolicity is recovered. We propose new statistical measures, defined in terms of turnover times, to distinguish between the transient and steady-states and apply these to experimental data from a landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites. Our study shows that catalysis at the molecular level with more than one enzyme always contains a non-classical regime and provides insight on how the classical limit is attained.

show abstract

Enzyme Kinetics at the Molecular Level

Cited by 6 publications

References 16 publications

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

Understanding the dynamics of complexin-situenzyme-catalyzed reactions undergoing mechanical stress

Transients generate memory and break hyperbolicity in stochastic enzymatic networks

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