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Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism

Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume—depending on the organism, the physiology, and the cellular compartment—is...

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Autores principales: Weilandt, Daniel R., Hatzimanikatis, Vassily
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Biophysical Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701012/
https://www.ncbi.nlm.nih.gov/pubmed/31311624
http://dx.doi.org/10.1016/j.bpj.2019.06.017
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author Weilandt, Daniel R.
Hatzimanikatis, Vassily
author_facet Weilandt, Daniel R.
Hatzimanikatis, Vassily
author_sort Weilandt, Daniel R.
collection PubMed
description Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume—depending on the organism, the physiology, and the cellular compartment—is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interfere with the interactions of enzymes and their reactants and thus affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new, to our knowledge, type of kinetics that captures and quantifies the effect of volume exclusion and other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of these kinetics using particle simulations. Our formulation, entitled generalized elementary kinetics, can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V(max) and the effective Michaelis-Menten constants K(M) under physiologically relevant volume occupancies. Finally, the effects of crowding were explored in the context of a linear pathway, with the finding that crowding can have a redistributing effect on the effective flux responses in the case of twofold enzyme overexpression. We suggest that this framework, in combination with detailed kinetics models, will improve our understanding of enzyme reaction networks under nonideal conditions.
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spelling pubmed-67010122020-07-23 Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism Weilandt, Daniel R. Hatzimanikatis, Vassily Biophys J Articles Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume—depending on the organism, the physiology, and the cellular compartment—is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interfere with the interactions of enzymes and their reactants and thus affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new, to our knowledge, type of kinetics that captures and quantifies the effect of volume exclusion and other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of these kinetics using particle simulations. Our formulation, entitled generalized elementary kinetics, can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V(max) and the effective Michaelis-Menten constants K(M) under physiologically relevant volume occupancies. Finally, the effects of crowding were explored in the context of a linear pathway, with the finding that crowding can have a redistributing effect on the effective flux responses in the case of twofold enzyme overexpression. We suggest that this framework, in combination with detailed kinetics models, will improve our understanding of enzyme reaction networks under nonideal conditions. The Biophysical Society 2019-07-23 2019-06-25 /pmc/articles/PMC6701012/ /pubmed/31311624 http://dx.doi.org/10.1016/j.bpj.2019.06.017 Text en © 2019 Biophysical Society. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles
Weilandt, Daniel R.
Hatzimanikatis, Vassily
Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title_full Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title_fullStr Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title_full_unstemmed Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title_short Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism
title_sort particle-based simulation reveals macromolecular crowding effects on the michaelis-menten mechanism
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701012/
https://www.ncbi.nlm.nih.gov/pubmed/31311624
http://dx.doi.org/10.1016/j.bpj.2019.06.017
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