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Comparison of methods for analyzing kinetic data from mechanism-based enzyme inactivation: Application to nitric oxide synthase

Overview of attention for article published in The AAPS Journal, March 2000
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Title
Comparison of methods for analyzing kinetic data from mechanism-based enzyme inactivation: Application to nitric oxide synthase
Published in
The AAPS Journal, March 2000
DOI 10.1208/ps020108
Pubmed ID
Authors

Tristan S. Maurer, Ho-Leung Fung

Abstract

The goals of this study were (1) to investigate the performance of 2 classical methods of kinetic analysis when applied to data from enzyme systems in which mechanism-based inactivation and enzyme degradation are present, and (2) to develop and validate a nonlinear method of kinetic data analysis that may perform better under these situations. A composite equation was derived to link various parameters that govern the kinetics of mechanism-based inactivation, viz., enzyme activity, inhibitor-binding affinity (K(I)), inactivation rate (k(inact)), and enzyme degradation (k(deg)). The relative accuracy and precision of parameter estimation by the Dixon and Kitz-Wilson methods and a new nonlinear method were evaluated by computer simulation. The behavior of these methods of analysis were validated experimentally, using the nitric oxide synthase enzyme, both in purified form and as expressed in murine macrophage cell cultures. We showed that the Dixon method, as expected, could not provide accurate estimates of K(I) in the presence of either enzyme inactivation or instability. The Kitz-Wilson method could provide accurate estimates of these parameters; however, the precisions of these estimates were poorer than those obtained using the nonlinear method of analysis. We conclude that the nonlinear approach is superior to classical methods of data analysis for enzyme inhibitor kinetics, based on better efficiency, accuracy, and precision.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 4%
United States 1 2%
India 1 2%
Australia 1 2%
Unknown 47 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 23%
Student > Ph. D. Student 11 21%
Student > Master 11 21%
Professor 5 10%
Student > Bachelor 3 6%
Other 5 10%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 31%
Chemistry 14 27%
Pharmacology, Toxicology and Pharmaceutical Science 5 10%
Biochemistry, Genetics and Molecular Biology 4 8%
Medicine and Dentistry 4 8%
Other 5 10%
Unknown 4 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 February 2019.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from The AAPS Journal
#520
of 1,463 outputs
Outputs of similar age
#13,754
of 41,736 outputs
Outputs of similar age from The AAPS Journal
#2
of 5 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,463 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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