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      Structure of allergens and structure based epitope predictions

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          Abstract

          The structure determination of major allergens is a prerequisite for analyzing surface exposed areas of the allergen and for mapping conformational epitopes. These may be determined by experimental methods including crystallographic and NMR-based approaches or predicted by computational methods. In this review we summarize the existing structural information on allergens and their classification in protein fold families. The currently available allergen-antibody complexes are described and the experimentally obtained epitopes compared. Furthermore we discuss established methods for linear and conformational epitope mapping, putting special emphasis on a recently developed approach, which uses the structural similarity of proteins in combination with the experimental cross-reactivity data for epitope prediction.

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          Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

          B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/. Proteins 2006. (c) 2006 Wiley-Liss, Inc.
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            Improved method for predicting linear B-cell epitopes

            Background B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. Results In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. Conclusion The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at .
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              The lipocalin protein family: structure and function.

              The lipocalin protein family is a large group of small extracellular proteins. The family demonstrates great diversity at the sequence level; however, most lipocalins share three characteristic conserved sequence motifs, the kernel lipocalins, while a group of more divergent family members, the outlier lipocalins, share only one. Belying this sequence dissimilarity, lipocalin crystal structures are highly conserved and comprise a single eight-stranded continuously hydrogen-bonded antiparallel beta-barrel, which encloses an internal ligand-binding site. Together with two other families of ligand-binding proteins, the fatty-acid-binding proteins (FABPs) and the avidins, the lipocalins form part of an overall structural superfamily: the calycins. Members of the lipocalin family are characterized by several common molecular-recognition properties: the ability to bind a range of small hydrophobic molecules, binding to specific cell-surface receptors and the formation of complexes with soluble macromolecules. The varied biological functions of the lipocalins are mediated by one or more of these properties. In the past, the lipocalins have been classified as transport proteins; however, it is now clear that the lipocalins exhibit great functional diversity, with roles in retinol transport, invertebrate cryptic coloration, olfaction and pheromone transport, and prostaglandin synthesis. The lipocalins have also been implicated in the regulation of cell homoeostasis and the modulation of the immune response, and, as carrier proteins, to act in the general clearance of endogenous and exogenous compounds.
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                Author and article information

                Contributors
                Journal
                Methods
                Methods
                Methods (San Diego, Calif.)
                Academic Press
                1046-2023
                1095-9130
                01 March 2014
                01 March 2014
                : 66
                : 1
                : 3-21
                Affiliations
                [a ]European Molecular Biology Laboratory, Hamburg Outstation, Hamburg, Germany
                [b ]ACIB (Austrian Centre of Industrial Biotechnology), Petersgasse 14, 8010 Graz, Austria
                [c ]Institute of Molecular Biosciences, University of Graz, Austria
                [d ]Institute of Chemistry, University of Graz, 8010 Graz, Austria
                Author notes
                [* ]Corresponding author. Fax: +43 316 3809897. walter.keller@ 123456uni-graz.at
                Article
                S1046-2023(13)00273-9
                10.1016/j.ymeth.2013.07.024
                3969231
                23891546
                f12f34e5-3b82-40ca-bac0-1ac401c6e9ce
                © 2013 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                Life sciences
                allergen structure,protein family,x-ray,nmr,ige epitope,structure based epitope prediction

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