Literature DB >> 11577687

A normalized plot as a novel and time-saving tool in complex enzyme kinetic analysis.

I G Bravo1, F Busto, D De Arriaga, M A Ferrero, L B Rodríguez-Aparicio, H Martínez-Blanco, A Reglero.   

Abstract

A new data treatment is described for designing kinetic experiments and analysing kinetic results for multi-substrate enzymes. Normalized velocities are plotted against normalized substrate concentrations. Data are grouped into n + 1 families across the range of substrate or product tested, n being the number of substrates plus products assayed. It has the following advantages over traditional methods: (1) it reduces to less than a half the amount of data necessary for a proper description of the system; (2) it introduces a self-consistency checking parameter that ensures the 'scientific reliability' of the mathematical output; (3) it eliminates the need for a prior knowledge of Vmax; (4) the normalization of data allows the use of robust and fuzzy methods suitable for managing really 'noisy' data; (5) it is appropriate for analysing complex systems, as the complete general equation is used, and the actual influence of effectors can be typified; (6) it is amenable to being implemented as a software that incorporates testing and electing among rival kinetic models.

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Year:  2001        PMID: 11577687      PMCID: PMC1222113          DOI: 10.1042/bj3580573

Source DB:  PubMed          Journal:  Biochem J        ISSN: 0264-6021            Impact factor:   3.857


  11 in total

1.  Parameter correlations while curve fitting.

Authors:  M L Johnson
Journal:  Methods Enzymol       Date:  2000       Impact factor: 1.600

2.  Outliers and robust parameter estimation.

Authors:  M L Johnson
Journal:  Methods Enzymol       Date:  2000       Impact factor: 1.600

3.  Artificial neural networks.

Authors:  W T Katz; J W Snell; M B Merickel
Journal:  Methods Enzymol       Date:  1992       Impact factor: 1.600

4.  Global analysis of biochemical and biophysical data.

Authors:  J M Beechem
Journal:  Methods Enzymol       Date:  1992       Impact factor: 1.600

5.  Parameter estimation by least-squares methods.

Authors:  M L Johnson; L M Faunt
Journal:  Methods Enzymol       Date:  1992       Impact factor: 1.600

6.  Use of weighting functions in data fitting.

Authors:  E Di Cera
Journal:  Methods Enzymol       Date:  1992       Impact factor: 1.600

7.  Combination plots as graphical tools in the study of enzyme inhibition.

Authors:  W W Chan
Journal:  Biochem J       Date:  1995-11-01       Impact factor: 3.857

8.  Regression analysis, experimental error, and statistical criteria in the design and analysis of experiments for discrimination between rival kinetic models.

Authors:  B Mannervik
Journal:  Methods Enzymol       Date:  1982       Impact factor: 1.600

9.  Statistical analysis of enzyme kinetic data.

Authors:  W W Cleland
Journal:  Methods Enzymol       Date:  1979       Impact factor: 1.600

10.  Kinetic properties of the acylneuraminate cytidylyltransferase from Pasteurella haemolytica A2.

Authors:  I G Bravo; S Barrallo; M A Ferrero; L B Rodríguez-Aparicio; H Martínez-Blanco; A Reglero
Journal:  Biochem J       Date:  2001-09-15       Impact factor: 3.857

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  2 in total

1.  Kinetic properties of the acylneuraminate cytidylyltransferase from Pasteurella haemolytica A2.

Authors:  I G Bravo; S Barrallo; M A Ferrero; L B Rodríguez-Aparicio; H Martínez-Blanco; A Reglero
Journal:  Biochem J       Date:  2001-09-15       Impact factor: 3.857

2.  Kinetic characterisation of arylamine N-acetyltransferase from Pseudomonas aeruginosa.

Authors:  Isaac M Westwood; Edith Sim
Journal:  BMC Biochem       Date:  2007-03-20       Impact factor: 4.059

  2 in total

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