Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.

<h4>Motivation</h4>The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to na...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Wen Zhang, Yanqing Niu, Yi Xiong, Meng Zhao, Rongwei Yu, Juan Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4c65f913da248c9bac135e9538bcf1d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b4c65f913da248c9bac135e9538bcf1d
record_format	dspace
spelling	oai:doaj.org-article:b4c65f913da248c9bac135e9538bcf1d2021-11-18T07:08:05ZComputational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.1932-620310.1371/journal.pone.0043575https://doaj.org/article/b4c65f913da248c9bac135e9538bcf1d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22927994/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Motivation</h4>The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.<h4>Methods</h4>In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.<h4>Results</h4>Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.<h4>Availability</h4>The web server and datasets are freely available at http://bcell.whu.edu.cn.Wen ZhangYanqing NiuYi XiongMeng ZhaoRongwei YuJuan LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e43575 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wen Zhang Yanqing Niu Yi Xiong Meng Zhao Rongwei Yu Juan Liu Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
description	<h4>Motivation</h4>The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.<h4>Methods</h4>In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.<h4>Results</h4>Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.<h4>Availability</h4>The web server and datasets are freely available at http://bcell.whu.edu.cn.
format	article
author	Wen Zhang Yanqing Niu Yi Xiong Meng Zhao Rongwei Yu Juan Liu
author_facet	Wen Zhang Yanqing Niu Yi Xiong Meng Zhao Rongwei Yu Juan Liu
author_sort	Wen Zhang
title	Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
title_short	Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
title_full	Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
title_fullStr	Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
title_full_unstemmed	Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
title_sort	computational prediction of conformational b-cell epitopes from antigen primary structures by ensemble learning.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b4c65f913da248c9bac135e9538bcf1d
work_keys_str_mv	AT wenzhang computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning AT yanqingniu computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning AT yixiong computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning AT mengzhao computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning AT rongweiyu computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning AT juanliu computationalpredictionofconformationalbcellepitopesfromantigenprimarystructuresbyensemblelearning
_version_	1718423936256966656

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.

Ejemplares similares