Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins

The number of existing protein sequences spans a very small fraction of sequence space. Natural proteins have overcome a strong negative selective pressure to avoid the formation of insoluble aggregates. Stably folded globular proteins and intrinsically disordered proteins (IDP) use alternative solutions to the aggregation problem. While in globular proteins folding minimizes the access to aggregation prone regions IDPs on average display large exposed contact areas. Here, we introduce the concept of average meta-structure correlation map to analyze sequence space. Using this novel conceptual view we show that representative ensembles of folded and ID proteins show distinct characteristics and responds differently to sequence randomization. By studying the way evolutionary constraints act on IDPs to disable a negative function (aggregation) we might gain insight into the mechanisms by which function - enabling information is encoded in IDPs.

Fate of Hydrophilic Nanoparticles in Biological Environments

The nanoparticles developed are based on chitosan, a biocompatible and biodegradable polysaccharide. The chitosan nanoparticles are formed in an entirely water-based process by electrostatic interactions with other biocompatible molecules. As a prerequisite to understand the fate of such nanoparticles in cells, comprehensive characterization and stability studies serve to identify quantitatively the impact of the raw material characteristics and preparation conditions on the nanoparticle characteristics. Methods included H-1 NMR spectroscopy, dilution viscometry, particle size analysis and electron microscopy. Cytotoxicity and cell uptake experiments on RAW 264.7 murine macrophages and p23 murine endothelial cells were performed to investigate the correlation with nanoparticle characteristics and effect of surface decoration with alginate. Cytotoxicity was assessed by the MTT survival test; cell uptake was monitored by fluorescent microscopy using labeled polymers.

Correlation of CXCL10, tumor necrosis factor receptor type II, and galectin 9 with disease activity in juvenile dermatomyositis.

OBJECTIVE: Juvenile dermatomyositis (DM) is a systemic autoimmune disorder of unknown immunopathogenesis in which the immune system targets the microvasculature of skeletal muscles, skin, and other organs. The current mainstay of therapy is a steroid regimen in combination with other immunosuppressive treatments. To date, no validated markers for monitoring disease activity have been identified, which hampers personalized treatment. This study was undertaken to identify a panel of proteins specifically related to active disease in juvenile DM. METHODS: We performed a multiplex immunoassay for plasma levels of 45 proteins related to inflammation in 25 patients with juvenile DM in 4 clinically well-defined groups, as determined by clinical activity and treatment. We compared them to 14 age-matched healthy children and 8 age-matched children with nonautoimmune muscle disease. RESULTS: Cluster analysis of circulating proteins showed distinct profiles for juvenile DM patients and controls based on a group of 10 proteins. In addition to CXCL10, tumor necrosis factor receptor type II (TNFRII) and galectin 9 were significantly increased in active juvenile DM. The levels of these 3 proteins were tightly linked to active disease and correlated with clinical scores (as measured by the Childhood Myositis Assessment Scale and physician's global assessment of disease activity on a visual analog scale). CONCLUSION: Our findings indicate that CXCL10, TNFRII, and galectin 9 correspond to disease status in juvenile DM and thus could be helpful in monitoring disease activity and guiding treatment. Furthermore, they might provide new knowledge about the pathogenesis of this autoimmune disease.