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A General Framework for Thermodynamically Consistent Parameterization and Efficient Sampling of Enzymatic Reactions

Overview of attention for article published in PLoS Computational Biology, April 2015
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Title
A General Framework for Thermodynamically Consistent Parameterization and Efficient Sampling of Enzymatic Reactions
Published in
PLoS Computational Biology, April 2015
DOI 10.1371/journal.pcbi.1004195
Pubmed ID
Authors

Pedro Saa, Lars K. Nielsen

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
Portugal 1 1%
Austria 1 1%
Unknown 93 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 23%
Researcher 15 16%
Student > Master 15 16%
Professor 9 9%
Student > Bachelor 7 7%
Other 18 19%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 24%
Engineering 16 17%
Biochemistry, Genetics and Molecular Biology 15 16%
Chemical Engineering 10 10%
Computer Science 4 4%
Other 14 15%
Unknown 14 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 April 2015.
All research outputs
#16,199,888
of 25,604,262 outputs
Outputs from PLoS Computational Biology
#7,008
of 9,014 outputs
Outputs of similar age
#151,785
of 279,764 outputs
Outputs of similar age from PLoS Computational Biology
#133
of 172 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,014 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,764 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 172 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.