Search

Miraculin differs from other sweet-tasting proteins because it is a taste-modifier having the unusual property of modifying sourness into sweetness. Its dimer is covalently linked by an inter-chain disulphide bond, and shows its taste-modifying activity at acidic pH, with maximum at pH 3.0, while it is flat at neutral pH. Previous studies suggested the importance of two histidine residues for the taste-modifying activity of miraculin. In this work, we have conducted molecular dynamics simulations on wild type miraculin and on three mutated dimers (H29A, H59A and H29A/H59A) both at neutral and acidic pH to investigate the structural and functional role of these two His residues. Our results suggested that at acidic pH the presence of two charged His at the interface induced a structural rearrangement of the two monomers, thus leading to their relative opening and the following adaptation of their conformation to the receptor surface. On the other hand the simulations on three mutants showed that the mutated dimers had a closed form, and highlighted the important role of H29 in stabilizing/destabilizing the dimer arrangement and also a cooperative effect of the two histidines., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Cutaneous melanoma is the most aggressive skin cancer; it is highly metastatic and responds poorly to current therapies. The expression of platelet-derived growth factor receptors (PDGF-Rs) is reported to be reduced in metastatic melanoma compared with benign nevi or normal skin; we then hypothesized that PDGF-Ralpha may control growth of melanoma cells. We show here that melanoma cells overexpressing PDGF-Ralpha respond to serum with a significantly lower proliferation compared with that of controls. Apoptosis, cell cycle arrest, pRb dephosphorylation, and DNA synthesis inhibition were also observed in cells overexpressing PDGF-Ralpha. Proliferation was rescued by PDGF-Ralpha inhibitors, allowing to exclude nonspecific toxic effects and indicating that PDGF-Ralpha mediates autocrine antiproliferation signals in melanoma cells. Accordingly, PDGF-Ralpha was found to mediate staurosporine cytotoxicity. A protein array-based analysis of the mitogen-activated protein kinase pathway revealed that melanoma cells overexpressing PDGF-Ralpha show a strong reduction of c-Jun phosphorylated in serine 63 and of protein phosphatase 2A/Balpha and a marked increase of p38gamma, mitogen-activated protein kinase kinase 3, and signal regulatory protein alpha1 protein expression. In a mouse model of primary melanoma growth, infection with the Ad-vector overexpressing PDGF-Ralpha reached a significant 70% inhibition of primary melanoma growth (P < .001) and a similar inhibition of tumor angiogenesis. All together, these data demonstrate that PDGF-Ralpha strongly impairs melanoma growth likely through autocrine mechanisms and indicate a novel endogenous mechanism involved in melanoma control.

Miraculin is a plant protein that displays the peculiar property of modifying taste by swiching sour into a sweet taste. Its monomer is flavourless at all pH as well as at high concentration; the dimer form elicits its taste-modifying activity at acidic pH; a tetrameric form is also reported as active. Two histidine residues, located in exposed regions, are the main responsible of miraculin activity, as demonstrated by mutagenesis studies. Since structural data of miraculin are not available, we have predicted its three-dimensional structure and simulated both its dimer and tetramer forms by comparative modelling and molecular docking techniques. Finally, molecular dynamics simulations at different pH conditions have indicated that at acidic pH the dimer assumes a widely open conformation, in agreement with the hypotheses coming from other studies.

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.

In order to simulate the conformational changes occurring when a protein interacts with its receptor, we firstly evaluated the structural differences between the experimental unbound and bound conformations for selected proteins and created theoretical complexes by replacing, in each experimental complex, the protein-bound with the protein-unbound chain. The theoretical models were then subjected to additional modeling refinements to improve the side chain geometry. Comparing the theoretical and experimental complexes in term of structural and energetic factors is resulted that the refined theoretical complexes became more similar to the experimental ones. We applied the same procedure within an homology modeling experiment, using as templates the experimental structures of human interleukin-1beta (IL-1beta) unbound and bound with its receptor, to build models of the homologous proteins from mouse and trout in unbound and bound conformations and to simulate the interaction with the related receptors. Our results suggest that homology modeling techniques are sensitive to differences between bound and unbound conformations, and that modeling with accuracy the side chains in the complex improves the interaction and molecular recognition. Moreover, our refinement procedure could be used in protein-protein interaction studies and, also, applied in conjunction with rigid-body docking when is not available the protein-bound conformation.

Amino acid propensities for secondary structures were used since the 1970s, when Chou and Fasman evaluated them within datasets of few tens of proteins and developed a method to predict secondary structure of proteins, still in use despite prediction methods having evolved to very different approaches and higher reliability. Propensity for secondary structures represents an intrinsic property of amino acid, and it is used for generating new algorithms and prediction methods, therefore our work has been aimed to investigate what is the best protein dataset to evaluate the amino acid propensities, either larger but not homogeneous or smaller but homogeneous sets, i.e., all-alpha, all-beta, alpha-beta proteins. As a first analysis, we evaluated amino acid propensities for helix, beta-strand, and coil in more than 2000 proteins from the PDBselect dataset. With these propensities, secondary structure predictions performed with a method very similar to that of Chou and Fasman gave us results better than the original one, based on propensities derived from the few tens of X-ray protein structures available in the 1970s. In a refined analysis, we subdivided the PDBselect dataset of proteins in three secondary structural classes, i.e., all-alpha, all-beta, and alpha-beta proteins. For each class, the amino acid propensities for helix, beta-strand, and coil have been calculated and used to predict secondary structure elements for proteins belonging to the same class by using resubstitution and jackknife tests. This second round of predictions further improved the results of the first round. Therefore, amino acid propensities for secondary structures became more reliable depending on the degree of homogeneity of the protein dataset used to evaluate them. Indeed, our results indicate also that all algorithms using propensities for secondary structure can be still improved to obtain better predictive results.