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Extracting kinetic parameters for homogeneous [Os(bpy)2ClPyCOOH]+ mediated enzyme reactions from cyclic voltammetry and simulations.

ABSTRACT: The homogeneous reaction between glucose oxidase and osmium bipyridine-pyridine carboxylic acid in the presence of glucose has been studied in detail by cyclic voltammetry and digital simulation. Combination of the analytical equations that describe the dependence of the amperometric response on enzyme, substrate and co-substrate concentrations for the limiting cases with digital simulation of the coupled enzyme reaction diffusion problem allows us to extract kinetic parameters for the substrate-enzyme reaction: K(MS)=10.8 mM, k(cat)=254 s(-1) and for the redox mediator-enzyme reaction, k=2.2x10(5) M(-1) s(-1). The accurate determination of the kinetic parameters at low substrate concentrations (<7 mM) is limited by depletion of the substrate close to the electrode surface. At high substrate concentrations (>20 mM) inactivation of the reduced form of glucose oxidase in the bulk solution must be taken into account in the analysis of the results.

SUBMITTER: Flexer V

PROVIDER: S-EPMC2742741 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Extracting kinetic parameters for homogeneous [Os(bpy)2ClPyCOOH]+ mediated enzyme reactions from cyclic voltammetry and simulations.

Flexer V V Ielmini M V MV Calvo E J EJ Bartlett P N PN

Bioelectrochemistry (Amsterdam, Netherlands) 20080812 1

The homogeneous reaction between glucose oxidase and osmium bipyridine-pyridine carboxylic acid in the presence of glucose has been studied in detail by cyclic voltammetry and digital simulation. Combination of the analytical equations that describe the dependence of the amperometric response on enzyme, substrate and co-substrate concentrations for the limiting cases with digital simulation of the coupled enzyme reaction diffusion problem allows us to extract kinetic parameters for the substrate ...[more]

PMID: 18824418

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Project description:BackgroundEnzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types.ResultsWe developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus.ConclusionsWith this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.

Dataset Information

Extracting kinetic parameters for homogeneous [Os(bpy)2ClPyCOOH]+ mediated enzyme reactions from cyclic voltammetry and simulations.

Publications

Extracting kinetic parameters for homogeneous [Os(bpy)2ClPyCOOH]+ mediated enzyme reactions from cyclic voltammetry and simulations.

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