Cargando…

Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous...

Descripción completa

Detalles Bibliográficos
Autores principales: Singh, Harinder, Ansari, Hifzur Rahman, Raghava, Gajendra P. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646881/
https://www.ncbi.nlm.nih.gov/pubmed/23667458
http://dx.doi.org/10.1371/journal.pone.0062216
_version_ 1782268662040756224
author Singh, Harinder
Ansari, Hifzur Rahman
Raghava, Gajendra P. S.
author_facet Singh, Harinder
Ansari, Hifzur Rahman
Raghava, Gajendra P. S.
author_sort Singh, Harinder
collection PubMed
description One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/).
format Online
Article
Text
id pubmed-3646881
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-36468812013-05-10 Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence Singh, Harinder Ansari, Hifzur Rahman Raghava, Gajendra P. S. PLoS One Research Article One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/). Public Library of Science 2013-05-07 /pmc/articles/PMC3646881/ /pubmed/23667458 http://dx.doi.org/10.1371/journal.pone.0062216 Text en © 2013 Singh et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Singh, Harinder
Ansari, Hifzur Rahman
Raghava, Gajendra P. S.
Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title_full Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title_fullStr Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title_full_unstemmed Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title_short Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
title_sort improved method for linear b-cell epitope prediction using antigen’s primary sequence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646881/
https://www.ncbi.nlm.nih.gov/pubmed/23667458
http://dx.doi.org/10.1371/journal.pone.0062216
work_keys_str_mv AT singhharinder improvedmethodforlinearbcellepitopepredictionusingantigensprimarysequence
AT ansarihifzurrahman improvedmethodforlinearbcellepitopepredictionusingantigensprimarysequence
AT raghavagajendraps improvedmethodforlinearbcellepitopepredictionusingantigensprimarysequence