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Identification of B-cell epitopes in an antigen for inducing specific class of antibodies

BACKGROUND: In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors’ knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, I...

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Autores principales: Gupta, Sudheer, Ansari, Hifzur Rahman, Gautam, Ankur, Raghava, Gajendra PS
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831251/
https://www.ncbi.nlm.nih.gov/pubmed/24168386
http://dx.doi.org/10.1186/1745-6150-8-27
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author Gupta, Sudheer
Ansari, Hifzur Rahman
Gautam, Ankur
Raghava, Gajendra PS
author_facet Gupta, Sudheer
Ansari, Hifzur Rahman
Gautam, Ankur
Raghava, Gajendra PS
author_sort Gupta, Sudheer
collection PubMed
description BACKGROUND: In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors’ knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, IgG) except allergenic epitopes (IgE). In this study, an attempt has been made to understand the relation between primary sequence of epitopes and the class of antibodies generated. RESULTS: The dataset used in this study has been derived from Immune Epitope Database and consists of 14725 B-cell epitopes that include 11981 IgG, 2341 IgE, 403 IgA specific epitopes and 22835 non-B-cell epitopes. In order to understand the preference of residues or motifs in these epitopes, we computed and compared amino acid and dipeptide composition of IgG, IgE, IgA inducing epitopes and non-B-cell epitopes. Differences in composition profiles of different classes of epitopes were observed, and few residues were found to be preferred. Based on these observations, we developed models for predicting antibody class-specific B-cell epitopes using various features like amino acid composition, dipeptide composition, and binary profiles. Among these, dipeptide composition-based support vector machine model achieved maximum Matthews correlation coefficient of 0.44, 0.70 and 0.45 for IgG, IgE and IgA specific epitopes respectively. All models were developed on experimentally validated non-redundant dataset and evaluated using five-fold cross validation. In addition, the performance of dipeptide-based model was also evaluated on independent dataset. CONCLUSION: Present study utilizes the amino acid sequence information for predicting the tendencies of antigens to induce different classes of antibodies. For the first time, in silico models have been developed for predicting B-cell epitopes, which can induce specific class of antibodies. A web service called IgPred has been developed to serve the scientific community. This server will be useful for researchers working in the field of subunit/epitope/peptide-based vaccines and immunotherapy (http://crdd.osdd.net/raghava/igpred/). REVIEWERS: This article was reviewed by Dr. M Michael Gromiha, Dr Christopher Langmead (nominated by Dr Robert Murphy) and Dr Lina Ma (nominated by Dr Zhang Zhang).
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spelling pubmed-38312512013-11-21 Identification of B-cell epitopes in an antigen for inducing specific class of antibodies Gupta, Sudheer Ansari, Hifzur Rahman Gautam, Ankur Raghava, Gajendra PS Biol Direct Research BACKGROUND: In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors’ knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, IgG) except allergenic epitopes (IgE). In this study, an attempt has been made to understand the relation between primary sequence of epitopes and the class of antibodies generated. RESULTS: The dataset used in this study has been derived from Immune Epitope Database and consists of 14725 B-cell epitopes that include 11981 IgG, 2341 IgE, 403 IgA specific epitopes and 22835 non-B-cell epitopes. In order to understand the preference of residues or motifs in these epitopes, we computed and compared amino acid and dipeptide composition of IgG, IgE, IgA inducing epitopes and non-B-cell epitopes. Differences in composition profiles of different classes of epitopes were observed, and few residues were found to be preferred. Based on these observations, we developed models for predicting antibody class-specific B-cell epitopes using various features like amino acid composition, dipeptide composition, and binary profiles. Among these, dipeptide composition-based support vector machine model achieved maximum Matthews correlation coefficient of 0.44, 0.70 and 0.45 for IgG, IgE and IgA specific epitopes respectively. All models were developed on experimentally validated non-redundant dataset and evaluated using five-fold cross validation. In addition, the performance of dipeptide-based model was also evaluated on independent dataset. CONCLUSION: Present study utilizes the amino acid sequence information for predicting the tendencies of antigens to induce different classes of antibodies. For the first time, in silico models have been developed for predicting B-cell epitopes, which can induce specific class of antibodies. A web service called IgPred has been developed to serve the scientific community. This server will be useful for researchers working in the field of subunit/epitope/peptide-based vaccines and immunotherapy (http://crdd.osdd.net/raghava/igpred/). REVIEWERS: This article was reviewed by Dr. M Michael Gromiha, Dr Christopher Langmead (nominated by Dr Robert Murphy) and Dr Lina Ma (nominated by Dr Zhang Zhang). BioMed Central 2013-10-30 /pmc/articles/PMC3831251/ /pubmed/24168386 http://dx.doi.org/10.1186/1745-6150-8-27 Text en Copyright © 2013 Gupta et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Gupta, Sudheer
Ansari, Hifzur Rahman
Gautam, Ankur
Raghava, Gajendra PS
Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title_full Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title_fullStr Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title_full_unstemmed Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title_short Identification of B-cell epitopes in an antigen for inducing specific class of antibodies
title_sort identification of b-cell epitopes in an antigen for inducing specific class of antibodies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831251/
https://www.ncbi.nlm.nih.gov/pubmed/24168386
http://dx.doi.org/10.1186/1745-6150-8-27
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