T 1 values in pure D 2 O and D 2 O solutions of human serum albumin (HSA) were measured versus temperature. A formula was derived based on H-H interactions between the surface HDO and non-exchangeable protein protons. The formula was used to evaluate the average distance of the interactions (r av ). The effective correlation times were then derived by replacing the experimental data in the formula. Short correlation times obtained for the solution with low HSA (0.02 g albumin for one ml of D 2 O) decreased from 53 to 29 ps, while longer times increased from 1.19 to 2.22 ns. They are of the order of a fraction of a nanosecond for the solution with high HSA (0.08 g albumin per one ml of D 2 O). The perfect consistency between the derived theory and experimental data indicates that the high-fi eld 1/T 1 in D 2 O solutions of albumin is caused by dipolar interactions between the surface HDO and non-exchangeable protein protons. It also suggests that the effective correlation time of the surface HDO is of the order of the mean lifetime of short-lived surface water.Introduction. Albumin is a major protein of serum; it performs several physiological functions, and it is also a carrier for several kinds of substances, drugs, and contrast agents [1-3]. Understanding relaxation mechanisms in albumin solutions is still of scientifi c interest. The water-protein interaction has extensively been studied by the nuclear magnetic relaxation dispersion (NMRD) technique, and also by the use of proton NMR, 2 H, and 17 O NMRD, multi dimension NMR, and X-ray diffraction techniques . However, the dynamics of surface water-protein interactions at high fi elds have not been understood completely.NMRD profi les of H 2 O and D 2 O solutions of albumin and other proteins show two dispersions, one at low fi elds and the other at much higher fi elds [15,17]. The low-fi eld dispersion was attributed to bound water, and it yields the rotational correlation time of proteins [6,14,15,17,21]. At the beginning, the small high-fi eld dispersion was attributed to surface water on a time scale of order one ns [13,15,17]. However, the lifetime of the majority of water around albumin varies from 10 to 100 ps [9,18,[26][27][28]. Therefore, T 1 relaxation at high fi elds may be explained in terms of such short-lived surface water. In fact, some new theories related to the lifetimes or local translation mobility or diffusive motions of short-lived surface water have recently been developed to explain the T 1 mechanism at the water-protein interface [9,21,29,30]. These theories are very sophisticated, and they involve various parameters. A new approach, based on the average value of H-H distances and of correlation times, should lead to a much simpler theory. Such an approach should be a further step if the derived theory reproduces previously suggested correlation times from experimental data.The aim of this work is to asses NMR T 1 mechanism in D 2 O solutions of human serum albumin (HSA) at 400 MHz. For this purpose, the observed T 1 in D 2 O solutions ...
Background Human serum albumin (HSA) is often selected as a subject of any study because albumin is the most abundant protein in human blood plasma. NMR is recognized as a valuable method to determine the structure of proteins-ligand and protein-drug complexes. Objective – Aim of the study In this study, protein drug interactions were investigated using 5-Fluorouracil anti-cancer drug and human serum albumin protein. Materials and methods In this context 400 MHz NMR spectrometry was used and NMR relaxation rates in drug-albumin complex were investigated with respect to increase albumin concentration and increase in 5-Fluorouracil (5-FU)-albumin solution temperature. Results The results of this study indicated that 5-FU had a weak association with albumin, and it easily dissociated from the protein to which it was attached. Conclusion The obtained results also gave us useful information about molecular dynamics of drug-albumin interactions.
We redesign the generalized pressure dark energy (GPDE) model, which is covering three common types of pressure parameterizations, with the help of a caloric framework to construct a theoretical ground for the machine learning (ML) analysis of cosmic Hubble parameter. The theoretical setup was optimized to find out appropriate values of its arbitrary parameters with the help of genetic neural network (GNN) algorithm and the most recent observational measurements of Hubble parameter. Since there is a shortcoming that the GNN process does not provide a direct method to calculate errors on the optimized values of free model parameters, we therefore take the Fisher Information Matrix (FIM) algorithm into account to deal with this issue. We see that the best-fitting value of Hubble constant and dimensionless dark energy density are in very good agreement with the most recent observations. Also, we discussed the optimized model from a cosmological perspective by making use of the evolutionary behavior of some cosmological parameters to present additional cosmological aspects of our theoretical proposal. It is concluded that our model implies physically meaningful results. In summary, the constructed model can explain the current accelerated expansion phase of the cosmos via Hubble parameter successfully.
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