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The Src Homology 3 (SH3) domains are small protein-protein interaction domains that bind proline-rich sequences and mediate a wide range of cell-signaling and other important biological processes. Since deregulated signaling pathways form the basis of many human diseases, the SH3 domains have been attractive targets for novel therapeutics. High-affinity ligands for SH3 domains have been designed; however, these have all been peptide-based and no examples of entirely nonpeptide SH3 ligands have previously been reported. Using the mouse Tec Kinase SH3 domain as a model system for structure-based ligand design, we have identified several simple heterocyclic compounds that selectively bind to the Tec SH3 domain. Using a combination of nuclear magnetic resonance chemical shift perturbation, structure-activity relationships, and site-directed mutagenesis, the binding of these compounds at the proline-rich peptide-binding site has been characterized. The most potent of these, 2-aminoquinoline, bound with Kd = 125 microM and was able to compete for binding with a proline-rich peptide. Synthesis of 6-substituted-2-aminoquinolines resulted in ligands with up to 6-fold improved affinity over 2-aminoquinoline and enhanced specificity for the Tec SH3 domain. Therefore, 2-aminoquinolines may potentially be useful for the development of high affinity small molecule ligands for SH3 domains.
PTK6 (also known as Brk) is an intracellular tyrosine kinase that contains SH3, SH2, and tyrosine kinase catalytic (Kinase) domains. The SH3 domain of PTK6 interacts with the N-terminal half of the linker (Linker) region between the SH2 and Kinase domains. Site-directed mutagenesis and surface plasmon resonance studies showed that a tryptophan residue (Trp44) in the SH3 domain and proline residues in the Linker region, in the order of Pro177, Pro175, and Pro179, contribute to the interaction. The three-dimensional modeled structure of the SH3-Linker complex was in agreement with the biochemical data. Disruption of the intramolecular interaction between the SH3 domain and the Linker region by mutation of Trp44, Pro175, Pro177, and Pro179 markedly increased the catalytic activity of PTK6 in HEK 293 cells. These results demonstrate that Trp44 in the SH3 domain and Pro177, Pro175, and Pro179 in the N-terminal half of the Linker region play important roles in the SH3-Linker interaction to maintain the protein in an inactive conformation along with the phosphorylated Tyr447-SH2 interaction.
The phosphoinositide 3-kinase (PI3K) is frequently activated in human cancer cells due to gain of function mutations in the catalytic (p110) and the regulatory (p85) subunits. The regulatory subunit consists of an SH3 domain and two SH2 domains. An oncogenic form of p85α named p65 lacking the c-terminal SH2 domain (cSH2) has been cloned from an irradiation-induced murine thymic lymphoma and transgenic mice expressing p65 in T lymphocytes develop a lymphoproliferative disorder. We have recently detected a c-terminal truncated form of p85α named p76α in a human lymphoma cell line lacking most of the cSH2 domain due to a frame shift mutation. Here, we report that the deletion of the cSH2 domain enhances the activating effects of the n-terminal SH2 domain (nSH2) mutants K379E and R340E on the PI3K/Akt pathway and micro tumor formation in a focus assay. Further analysis revealed that this transforming effect is mediated by activation of the catalytic PI3K isoform p110α and downstream signaling through mTOR. Our data further support a mechanistic model in which mutations of the cSH2 domain of p85α can abrogate its negative regulatory function on PI3K activity via the nSH2 domain of p85α. Copyright 2012 Elsevier Inc. All rights reserved.
SH3 domains are small protein modules that are involved in protein-protein interactions in several essential metabolic pathways. The availability of the complete genome and the limited number of clearly identifiable SH3 domains make the yeast Saccharomyces cerevisae an ideal proteomic-based model system to investigate the structural rules dictating the SH3-mediated protein interactions and to develop new tools to assist these studies. In the present work, we have determined the solution structure of the SH3 domain from Myo3 and modeled by homology that of the highly homologous Myo5, two myosins implicated in actin polymerization. We have then implemented an integrated approach that makes use of experimental and computational methods to characterize their binding properties. While accommodating their targets in the classical groove, the two domains have selectivity in both orientation and sequence specificity of the target peptides. From our study, we propose a consensus sequence that may provide a useful guideline to identify new natural partners and suggest a strategy of more general applicability that may be of use in other structural proteomic studies.
Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was 153554b96e