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      Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull

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          Abstract

          The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.

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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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            A semi-empirical method for prediction of antigenic determinants on protein antigens.

            Analysis of data from experimentally determined antigenic sites on proteins has revealed that the hydrophobic residues Cys, Leu and Val, if they occur on the surface of a protein, are more likely to be a part of antigenic sites. A semi-empirical method which makes use of physicochemical properties of amino acid residues and their frequencies of occurrence in experimentally known segmental epitopes was developed to predict antigenic determinants on proteins. Application of this method to a large number of proteins has shown that our method can predict antigenic determinants with about 75% accuracy which is better than most of the known methods. This method is based on a single parameter and thus very simple to use.
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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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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 August 2015
                2015
                : 10
                : 8
                : e0134835
                Affiliations
                [1 ]School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
                [2 ]State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, People’s Republic of China
                [3 ]Department of Physical Sciences, Grant MacEwan University, Alberta, Canada
                Vrije Universiteit Brussel, BELGIUM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JG. Performed the experiments: WZ. Analyzed the data: JG WZ JR GH KW MH. Contributed reagents/materials/analysis tools: JG WZ JR GH KW. Wrote the paper: JG WZ JR GH KW MH. Designed the software used in analysis: ZW JG.

                Article
                PONE-D-15-02116
                10.1371/journal.pone.0134835
                4526569
                26244562
                5ef6bd1a-5607-4919-b1c4-20385e68de6c
                Copyright @ 2015

                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

                History
                : 27 February 2015
                : 14 July 2015
                Page count
                Figures: 9, Tables: 1, Pages: 16
                Funding
                JG was supported by Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP grant number 20130031120001). WZ was supported by the Ph.D. Candidate Research Innovation Fund of Nankai University (no. 68150003). JR was supported by the International Development Research Center, Ottawa, Canada (no. 104519-010) and the National Natural Science Foundation of China (NSFC, grant numbers 31050110432, 31150110577). GH was supported by NSFC (grant number 11101226). KW was supported by NSFC (grant number 11301286). This work was also supported by the National Natural Science Foundation of China (NSFC, grant number 61170099). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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