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Background
Minor changes in protein structure induced by small organic and inorganic molecules can result in significant metabolic effects. The effects can be even more profound if the molecular players are chemically active and present in the cell in considerable amounts. The aim of our study was to investigate effects of a nitric oxide donor (spermine NONOate), ATP and sodium/potassium environment on the dynamics of thermal unfolding of human hemoglobin (Hb). The effect of these molecules was examined by means of circular dichroism spectrometry (CD) in the temperature range between 25°C and 70°C. The alpha-helical content of buffered hemoglobin samples (0.1 mg/ml) was estimated via ellipticity change measurements at a heating rate of 1°C/min.
Results
Major results were:
1) spermine NONOate persistently decreased the hemoglobin unfolding temperature T u irrespectively of the Na + /K + environment,
2) ATP instead increased the unfolding temperature by 3°C in both sodium-based and potassium-based buffers and
3) mutual effects of ATP and NO were strongly influenced by particular buffer ionic compositions. Moreover, the presence of potassium facilitated a partial unfolding of alpha-helical structures even at room temperature.
Conclusion
The obtained data might shed more light on molecular mechanisms and biophysics involved in the regulation of protein activity by small solutes in the cell.
The paper deals with the asymptotic behaviour of estimators, statistical tests and confidence intervals for L²-distances to uniformity based on the empirical distribution function, the integrated empirical distribution function and the integrated empirical survival function. Approximations of power functions, confidence intervals for the L²-distances and statistical neighbourhood-of-uniformity validation tests are obtained as main applications. The finite sample behaviour of the procedures is illustrated by a simulation study.
On the basis of bivariate data, assumed to be observations of independent copies of a random vector (S,N), we consider testing the hypothesis that the distribution of (S,N) belongs to the parametric class of distributions that arise with the compound Poisson exponential model. Typically, this model is used in stochastic hydrology, with N as the number of raindays, and S as total rainfall amount during a certain time period, or in actuarial science, with N as the number of losses, and S as total loss expenditure during a certain time period. The compound Poisson exponential model is characterized in the way that a specific transform associated with the distribution of (S,N) satisfies a certain differential equation. Mimicking the function part of this equation by substituting the empirical counterparts of the transform we obtain an expression the weighted integral of the square of which is used as test statistic. We deal with two variants of the latter, one of which being invariant under scale transformations of the S-part by fixed positive constants. Critical values are obtained by using a parametric bootstrap procedure. The asymptotic behavior of the tests is discussed. A simulation study demonstrates the performance of the tests in the finite sample case. The procedure is applied to rainfall data and to an actuarial dataset. A multivariate extension is also discussed.
In a special paired sample case, Hotelling’s T² test based on the differences of the paired random vectors is the likelihood ratio test for testing the hypothesis that the paired random vectors have the same mean; with respect to a special group of affine linear transformations it is the uniformly most powerful invariant test for the general alternative of a difference in mean. We present an elementary straightforward proof of this result. The likelihood ratio test for testing the hypothesis that the covariance structure is of the assumed special form is derived and discussed. Applications to real data are given.