A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions.
Kinetic models provide the means to understand and predict the dynamic behaviour of enzymes upon different perturbations. Despite their obvious advantages, classical parameterizations require large amounts of data to fit their parameters. Particularly, enzymes displaying complex reaction and regulat...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4397067?pdf=render |
| _version_ | 1818577611610128384 |
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| author | Pedro Saa Lars K Nielsen |
| author_facet | Pedro Saa Lars K Nielsen |
| author_sort | Pedro Saa |
| collection | DOAJ |
| description | Kinetic models provide the means to understand and predict the dynamic behaviour of enzymes upon different perturbations. Despite their obvious advantages, classical parameterizations require large amounts of data to fit their parameters. Particularly, enzymes displaying complex reaction and regulatory (allosteric) mechanisms require a great number of parameters and are therefore often represented by approximate formulae, thereby facilitating the fitting but ignoring many real kinetic behaviours. Here, we show that full exploration of the plausible kinetic space for any enzyme can be achieved using sampling strategies provided a thermodynamically feasible parameterization is used. To this end, we developed a General Reaction Assembly and Sampling Platform (GRASP) capable of consistently parameterizing and sampling accurate kinetic models using minimal reference data. The former integrates the generalized MWC model and the elementary reaction formalism. By formulating the appropriate thermodynamic constraints, our framework enables parameterization of any oligomeric enzyme kinetics without sacrificing complexity or using simplifying assumptions. This thermodynamically safe parameterization relies on the definition of a reference state upon which feasible parameter sets can be efficiently sampled. Uniform sampling of the kinetics space enabled dissecting enzyme catalysis and revealing the impact of thermodynamics on reaction kinetics. Our analysis distinguished three reaction elasticity regions for common biochemical reactions: a steep linear region (0> ΔGr >-2 kJ/mol), a transition region (-2> ΔGr >-20 kJ/mol) and a constant elasticity region (ΔGr <-20 kJ/mol). We also applied this framework to model more complex kinetic behaviours such as the monomeric cooperativity of the mammalian glucokinase and the ultrasensitive response of the phosphoenolpyruvate carboxylase of Escherichia coli. In both cases, our approach described appropriately not only the kinetic behaviour of these enzymes, but it also provided insights about the particular features underpinning the observed kinetics. Overall, this framework will enable systematic parameterization and sampling of enzymatic reactions. |
| first_indexed | 2024-12-16T06:32:40Z |
| format | Article |
| id | doaj.art-049bae28833e476bbbe9e42b2b1c9699 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:32:40Z |
| publishDate | 2015-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-049bae28833e476bbbe9e42b2b1c96992022-12-21T22:40:51ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-04-01114e100419510.1371/journal.pcbi.1004195A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions.Pedro SaaLars K NielsenKinetic models provide the means to understand and predict the dynamic behaviour of enzymes upon different perturbations. Despite their obvious advantages, classical parameterizations require large amounts of data to fit their parameters. Particularly, enzymes displaying complex reaction and regulatory (allosteric) mechanisms require a great number of parameters and are therefore often represented by approximate formulae, thereby facilitating the fitting but ignoring many real kinetic behaviours. Here, we show that full exploration of the plausible kinetic space for any enzyme can be achieved using sampling strategies provided a thermodynamically feasible parameterization is used. To this end, we developed a General Reaction Assembly and Sampling Platform (GRASP) capable of consistently parameterizing and sampling accurate kinetic models using minimal reference data. The former integrates the generalized MWC model and the elementary reaction formalism. By formulating the appropriate thermodynamic constraints, our framework enables parameterization of any oligomeric enzyme kinetics without sacrificing complexity or using simplifying assumptions. This thermodynamically safe parameterization relies on the definition of a reference state upon which feasible parameter sets can be efficiently sampled. Uniform sampling of the kinetics space enabled dissecting enzyme catalysis and revealing the impact of thermodynamics on reaction kinetics. Our analysis distinguished three reaction elasticity regions for common biochemical reactions: a steep linear region (0> ΔGr >-2 kJ/mol), a transition region (-2> ΔGr >-20 kJ/mol) and a constant elasticity region (ΔGr <-20 kJ/mol). We also applied this framework to model more complex kinetic behaviours such as the monomeric cooperativity of the mammalian glucokinase and the ultrasensitive response of the phosphoenolpyruvate carboxylase of Escherichia coli. In both cases, our approach described appropriately not only the kinetic behaviour of these enzymes, but it also provided insights about the particular features underpinning the observed kinetics. Overall, this framework will enable systematic parameterization and sampling of enzymatic reactions.http://europepmc.org/articles/PMC4397067?pdf=render |
| spellingShingle | Pedro Saa Lars K Nielsen A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. PLoS Computational Biology |
| title | A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. |
| title_full | A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. |
| title_fullStr | A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. |
| title_full_unstemmed | A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. |
| title_short | A general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions. |
| title_sort | general framework for thermodynamically consistent parameterization and efficient sampling of enzymatic reactions |
| url | http://europepmc.org/articles/PMC4397067?pdf=render |
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