NMR investigations of protein-carbohydrate interactions: refined three- dimensional structure of the complex between hevein and methyl beta- chitobioside

The specific interaction of hevein with GlcNAc-containing oligosaccharides has been analyzed by1H-NMR spectroscopy. The association constants for the binding of hevein to a variety of ligands have been estimated from1H-NMR titration experiments. The association constants increase in the order GlcNAc-alpha(1-->6)-Man < GlcNAc < benzyl-beta-GlcNAc < p-nitrophenyl-beta-GlcNAc < chitobiose < p- nitrophenyl-beta-chitobioside < methyl-beta-chitobioside < chitotriose. Entropy and enthalpy of binding for different complexes have been obtained from van't Hoff analysis. The driving force for the binding process is provided by a negative DeltaH0which is partially compensated by negative DeltaS0. These negative signs indicate that hydrogen bonding and van der Waals forces are the major interactions stabilizing the complex. NOESY NMR experiments in water solution provided 475 accurate protein proton-proton distance constraints after employing the MARDIGRAS program. In addition, 15 unambiguous protein/carbohydrate NOEs were detected. All the experimental constraints were used in a refinement protocol including restrained molecular dynamics in order to determine the highly refined solution conformation of this protein- carbohydrate complex. With regard to the NMR structure of the free protein, no important changes in the protein nOe's were observed, indicating that carbohydrate-induced conformational changes are small. The average backbone rmsd of the 20 refined structures was 0.055 nm, while the heavy atom rmsd was 0.116 nm. It can be deduced that both hydrogen bonds and van der Waals contacts confer stability to the complex. A comparison of the three-dimensional structure of hevein in solution to those reported for wheat germ agglutinin (WGA) and hevein itself in the solid state has also been performed. The polypeptide conformation has also been compared to the NMR-derived structure of a smaller antifungical peptide, Ac-AMP2.      

Immunoexpression of aquaporins 1, 2, 3 and 4 in kidney of yak (Bos grunniens) on the Qinghai-Tibetan Plateau

Aquaporins (AQP) 1, 2, 3 and 4 belong to the aquaporin water channel family and play an important role in urine concentration by reabsorption of water from renal tubule fluid. Renal AQPs have not been reported in the yak (Bos grunniens), which resides in the Qinghai Tibetan Plateau. We investigated AQPs 1?4 expressions in the kidneys of Yak using immunohistochemical staining. AQP1 was expressed mainly in the basolateral and apical membranes of the proximal tubules and descending thin limb of the loop of Henle. AQP2 was detected in the apical plasma membranes of collecting ducts and distal convoluted tubules. AQP3 was located in the proximal tubule, distal tubule and collecting ducts. AQP4 was located in the collecting ducts, distal straight tubule, glomerular capillaries and peritubular capillaries. The expression pattern of AQPs 1?4 in kidney of yak was different from other species, which possibly is related to kidney function in a high altitude environment.     

Combining protein evolution and secondary structure

An evolutionary model that combines protein secondary structure and amino acid replacement is introduced. It allows likelihood analysis of aligned protein sequences and does not require the underlying secondary (or tertiary) structures of these sequences to be known. One component of the model describes the organization of secondary structure along a protein sequence and another specifies the evolutionary process for each category of secondary structure. A database of proteins with known secondary structures is used to estimate model parameters representing these two components. Phylogeny, the third component of the model, can be estimated from the data set of interest. As an example, we employ our model to analyze a set of sucrose synthase sequences. For the evolution of sucrose synthase, a parametric bootstrap approach indicates that our model is statistically preferable to one that ignores secondary structure.