scholarly journals Progress-curve equations for reversible enzyme-catalysed reactions inhibited by tight-binding inhibitors

1990 ◽  
Vol 265 (3) ◽  
pp. 647-653 ◽  
Author(s):  
S E Szedlacsek ◽  
V Ostafe ◽  
R G Duggleby ◽  
M Serban ◽  
M O Vlad
Keyword(s):  
Linear Regression ◽  
Competitive Inhibition ◽  
Tight Binding ◽  
The Other ◽  
First Order ◽  
Regression Methods ◽  
Progress Curve ◽  
Dead End ◽  
Non Linear ◽  
Inhibition Constants

The rate equation for a tight-binding inhibitor of an enzyme-catalysed first-order reversible reaction was used to derive two integrated equations. One of them covers the situations in which competitive, uncompetitive or non-competitive inhibition occurs and the other refers to the special non-competitive case where the two inhibition constants are equal. For these equations, graphical and non-linear regression methods are proposed for distinguishing between types of inhibition and for calculating inhibition constants from progress-curve data. The application of the non-linear regression to the analysis of stimulated progress curves in the presence of a tight-binding inhibitor is also presented. The results obtained are valid for any type of ‘dead-end’-complex-forming inhibitor and can be used to characterize an unknown inhibitor on the basis of progress curves.

scholarly journals Graphical and statistical analysis of hyperbolic tight-binding inhibition

1987 ◽  
Vol 244 (3) ◽  
pp. 793-796 ◽  
Author(s):  
A Baici
Keyword(s):  
Statistical Analysis ◽  
Linear Regression ◽  
Competitive Inhibition ◽  
Tight Binding ◽  
General Character ◽  
Regression Methods ◽  
Leucocyte Elastase ◽  
Binding Inhibition ◽  
Non Linear ◽  
Kinetic Mechanisms

Equations are described for the analysis of enzyme-catalysed reactions in the presence of hyperbolic tight-binding inhibitors using both graphical and non-linear-regression methods. The general character of the equations allows the interpretation of several other kinetic mechanisms. As an example, the tight-binding hyperbolic non-competitive inhibition of human leucocyte elastase by a polysulphated glycosaminoglycan is analysed.

scholarly journals Kinetic mechanism of the dimeric ATP sulfurylase from plants

2013 ◽  
Vol 33 (4) ◽  
Author(s):  
Geoffrey E. Ravilious ◽  
Jonathan Herrmann ◽  
Soon Goo Lee ◽  
Corey S. Westfall ◽  
Joseph M. Jez
Keyword(s):  
Initial Velocity ◽  
Competitive Inhibition ◽  
Tight Binding ◽  
Atp Synthesis ◽  
Kinetic Mechanism ◽  
Titration Calorimetry ◽  
Atp Sulfurylase ◽  
Dead End ◽  
Sequential Mechanism ◽  
Enzyme Functions

In plants, sulfur must be obtained from the environment and assimilated into usable forms for metabolism. ATP sulfurylase catalyses the thermodynamically unfavourable formation of a mixed phosphosulfate anhydride in APS (adenosine 5′-phosphosulfate) from ATP and sulfate as the first committed step of sulfur assimilation in plants. In contrast to the multi-functional, allosterically regulated ATP sulfurylases from bacteria, fungi and mammals, the plant enzyme functions as a mono-functional, non-allosteric homodimer. Owing to these differences, here we examine the kinetic mechanism of soybean ATP sulfurylase [GmATPS1 (Glycine max (soybean) ATP sulfurylase isoform 1)]. For the forward reaction (APS synthesis), initial velocity methods indicate a single-displacement mechanism. Dead-end inhibition studies with chlorate showed competitive inhibition versus sulfate and non-competitive inhibition versus APS. Initial velocity studies of the reverse reaction (ATP synthesis) demonstrate a sequential mechanism with global fitting analysis suggesting an ordered binding of substrates. ITC (isothermal titration calorimetry) showed tight binding of APS to GmATPS1. In contrast, binding of PPi (pyrophosphate) to GmATPS1 was not detected, although titration of the E•APS complex with PPi in the absence of magnesium displayed ternary complex formation. These results suggest a kinetic mechanism in which ATP and APS are the first substrates bound in the forward and reverse reactions, respectively.

Modeling of phenol adsorption isotherm onto activated carbon by non-linear regression methods: models with three and four parameters

2018 ◽  
Vol 136 ◽  
pp. 199-206
Author(s):  
Donald Raoul Tchuifon Tchuifon ◽  
George Nche Ndifor-Angwafor ◽  
Aurelien Bopda ◽  
Solomon Gabche Anagho
Keyword(s):  
Activated Carbon ◽  
Adsorption Isotherm ◽  
Linear Regression ◽  
Phenol Adsorption ◽  
Regression Methods ◽  
Non Linear

scholarly journals PENGARUH MOTIVASI TERHADAP KEPUASAN KERJA PADA KARYAWAN KPRI "PERTAGUMA" KOTA MADIUN

2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Devie Putri Wijayanti
Keyword(s):  
Job Satisfaction ◽  
Linear Regression ◽  
Positive Influence ◽  
The Other ◽  
Simple Linear Regression ◽  
Exact Test ◽  
Regression Methods ◽  
Employee Job Satisfaction ◽  
Other Hand ◽  
Valid Instrument

This study aims to (1) To know the motivation  of employees KPRI  "Pertaguma" Madiun,   (2) To know  employee job  satisfaction  on KPRI  "Pertaguma"    Madiun,   and (3) To know  the motivation  is there  any  influence  on job  satisfaction   in employees    KPRI "Pertaguma" Madiun.  The samples in this study using a sample that is saturated  cooperative employee   is 17. Data collection  using  interviews  and questionnaires.   Data analysed use simple  linear regression  statistical  methods,  and to test whether  or not valid  instrument used regression  methods.  The results  showed  that motivation  has a positive  influence on employee job  satisfaction  on KPRI  "Pertaguma"    Madiun.  It is derived   from the value of Fisher's  exact test, whereas  the Fcount value of 64,792  Ftabel value of 4,54.  On the other hand Sig(hit) known values of 0.000  and 0.05 known  Sig(prob) value. This means that the Fvalue >= Ftabel (64.792 >= 4.54)  or Sig(hit) <= Sig(prob) (0.05 <= 0.000).   Meaning  a rejection  of H0 which  shows  that there  is an influence  of motivation   on job  satisfaction   in employees KPRI  "Pertaguma"     Madiun.  In addition  to the values  obtained  with the  t, t(test) value  is 8.049  while the value ttable 2,131.  On the other hand Sig(hit) value of 0.000  and Sig(prob) value as 0.05. This means that t >= ttable (8.049 >= 2.131) or <= Sig(hit) Sig(prob) (0.05 <= 0.000).  It means that H0    is rejected,  meaning  that motivation   has a different  effect on employee job  satisfaction on KPRI “Pertaguma” Madiun.

A Note on the Disadvantages Associated with the Treatment of an Eigenvalue as an Additional Dependent Variable in Differential Eigenvalue Problems

1968 ◽  
Vol 72 (696) ◽  
pp. 1068
Author(s):  
B. Dawson ◽  
M. Davies
Keyword(s):  
Differential Equations ◽  
Boundary Conditions ◽  
Eigenvalue Problems ◽  
The Other ◽  
Unknown Boundary ◽  
Boundary Values ◽  
First Order ◽  
Basic Set ◽  
Non Linear ◽  
First Order Differential Equations

A novel technique of dealing with differential eigenvalue problems has recently been introduced by Wadsworth and Wilde . The differential equation is expressed as a set of simultaneous first-order differential equations, the eigenvalueλbeing regarded as an additional variable by adding the equationto the basic set. The differential eigenvalue problem is thus reduced to a set of non-linear first-order differential equations with two-point boundary conditions. This treatment of the problem, although novel, suffers from two serious disadvantages. First, it introduces non-linearity into an otherwise linear set of equations. Thus, the solution can no longer be obtained by linear combinations of independent particular solutions. One method of solving the non-linear systems is by assigning arbitrary starting values at one boundary and performing a step-by-step integration to the other boundary where in general the boundary conditions are not satisfied. The problem can be solved by adjustment of the initial assigned arbitrary values until the given conditions at the other boundary are satisfied. A second method and the one used by Wadsworth and Wilde is to estimate the unknown boundary values at both boundaries and integrate inwards to a meeting point. Changes can then be made to the unknown boundary values to make the two branches of the curve fit together.

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