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VisualEnzymics offers analysis for five types of enzyme
kinetic data and includes 70 model equations:
- one substrate rate saturation data
- one substrate one inhibitor data
- pH rate profiles
- exponential data
- dose response data
Each analysis module holds up to 10 separate data sets
and each module has its own graph window with specially
formatted graphs that match the type of analysis. All data
sets can be graphed individually, or multiple data sets can
be overlaid in a single graph. Estimates and fit curves are
automatically updated and linked to each graph window. |
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One Substrate l
One Substrate One Inhibitor l
pH l
Exponentials l
Dose Response |
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VisualEnzymics provides 10 equations for fitting steady state rate
saturation profiles. These include Michaelis-Menten,
Michaelis-Menton plus offset, Michaelis-Menten plus
linear phase, Hill, Hill plus offset, substrate
inhibition, two site, two/one, sigmoid, and cubic.
These equations describe a variety of hyperbolic and
sigmoidal saturation profiles, and can be fitted to
almost any type of rate saturation data. The
equations all contain two, three, or four
parameters. Parameters can be floated or
individually held constant during fitting. Data can
be weighted four different ways, including constant
weighting, proportional weighting, between constant
and proportional, or by standard deviation. You can
choose one of four different fitting algorithms,
including Levenberg-Marquardt, Levenberg-Marquardt
Robust, Monte Carlo, and Monte Carlo Robust.
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VisualEnzymics provides nine equations for fitting
steady state inhibition data. These include
competitive, noncompetitive, uncompetitive,
hyperbolic competitive, hyperbolic noncompetitive,
hyperbolic uncompetitive, sigmoidal competitive,
sigmoidal noncompetitive, and sigmoidal
uncompetitive. These equations will fit inhibition
mechanisms for Michaelis-Menten type enzyme
kinetics, and for enzymes displaying cooperative
behavior. Enter data for an inhibition experiment as
substrate concentration, inhibitor concentration,
velocity, and standard deviation of the velocity.
VisualEnzymics will automatically parse the data
according to inhibitor concentration, and plot your
results as groups of data at each inhibitor
concentration. One click buttons will generate
automatic data transforms to Lineweaver-Burk,
Hanes-Woolf, Woolf-Hofstee, or Eadie-Scatchard
formats. Graphs can be exported in 5 different
graphic formats for electronic presentations or
publication in journals. |
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VisualEnzymics provides 16 pH rate profile equations
for fitting the pH dependence of binding or kinetic
parameters. All enzymes and proteins are sensitive to protons, and the variation
of reaction parameters as a function of pH yields
information about the titratable groups that
participate in binding and catalysis. These
reactions may involve acidic or basic groups, and
may yield various curve shapes as a function of pH.
VisualEnzymics provides equations describing single
or multiple inflection point pH titration curves.
Data may be plotted as Y versus pH, or Log Y versus
pH. Since pH dependence plots typically involve
parameter variation over several orders or
magnitude, VisualEnzymics offers proportional
weighting to achieve more accurate fits to data at
the extreme of the pH profile. Data may be fitted
with either the Levenberg-Marquardt or Monte Carlo
fitting algorithms. Multiple data plots can be
overlaid on the same graph to compare data from
different experiments. Plots can be combined in page
layouts for publication or lab notebooks.
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VisualEnzymics provides 24 equations for fitting
exponential data. There are eight equations for
single exponential data plus baseline, eight
equations for the sum of two exponentials plus
baseline, and eight equations for the sum of three
exponentials plus baseline. The exponential
equations will fit any variety of curve shape that
follows exponential behavior. Exponential behavior
may derive from transient kinetics in the form of
response versus time data, or may derive from
physical processes such as radioactive decay. All
fits yield the observed rate constant and the
amplitude of the exponential. When the initial
estimates of the rate constants are unknown, the
data can be fitted by the Monte Carlo method to
obtain good initial estimates. The fit then can be
optimized further by using the initial estimates in
the Levenberg-Marquart fitting algorithm. |
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VisualEnzymics provides eleven equations for fitting
dose response data. These types of equations can be
used to fit activity versus ligand concentration
data when the enzyme has not been purified from a
more complex biochemical system, or where the
response mechanism is unknown, or where the response
depends on biochemical interactions beyond the
enzyme itself. These equations provide three, four,
and five parameter logistic fits. Logistic equations
yield the minimum, maximum, half-saturation point,
slope of the inflection point, and skewness of a
dose dependent response. The shapes of these curves
can be hyperbolic or sigmoidal in decreasing or
increasing direction. Fits to these equations will
yield the fitted parameters and standard error of
the parameters, as well as the user-specified
confidence interval.
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