Cargando…

SEPPA: a computational server for spatial epitope prediction of protein antigens

In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Jing, Wu, Di, Xu, Tianlei, Wang, Xiaojing, Xu, Xiaolian, Tao, Lin, Li, Y. X., Cao, Z. W.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703964/
https://www.ncbi.nlm.nih.gov/pubmed/19465377
http://dx.doi.org/10.1093/nar/gkp417
_version_ 1782168896037453824
author Sun, Jing
Wu, Di
Xu, Tianlei
Wang, Xiaojing
Xu, Xiaolian
Tao, Lin
Li, Y. X.
Cao, Z. W.
author_facet Sun, Jing
Wu, Di
Xu, Tianlei
Wang, Xiaojing
Xu, Xiaolian
Tao, Lin
Li, Y. X.
Cao, Z. W.
author_sort Sun, Jing
collection PubMed
description In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of ‘unit patch of residue triangle’ was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported.
format Text
id pubmed-2703964
institution National Center for Biotechnology Information
language English
publishDate 2009
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-27039642009-07-01 SEPPA: a computational server for spatial epitope prediction of protein antigens Sun, Jing Wu, Di Xu, Tianlei Wang, Xiaojing Xu, Xiaolian Tao, Lin Li, Y. X. Cao, Z. W. Nucleic Acids Res Articles In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of ‘unit patch of residue triangle’ was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported. Oxford University Press 2009-07-01 2009-05-22 /pmc/articles/PMC2703964/ /pubmed/19465377 http://dx.doi.org/10.1093/nar/gkp417 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Sun, Jing
Wu, Di
Xu, Tianlei
Wang, Xiaojing
Xu, Xiaolian
Tao, Lin
Li, Y. X.
Cao, Z. W.
SEPPA: a computational server for spatial epitope prediction of protein antigens
title SEPPA: a computational server for spatial epitope prediction of protein antigens
title_full SEPPA: a computational server for spatial epitope prediction of protein antigens
title_fullStr SEPPA: a computational server for spatial epitope prediction of protein antigens
title_full_unstemmed SEPPA: a computational server for spatial epitope prediction of protein antigens
title_short SEPPA: a computational server for spatial epitope prediction of protein antigens
title_sort seppa: a computational server for spatial epitope prediction of protein antigens
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703964/
https://www.ncbi.nlm.nih.gov/pubmed/19465377
http://dx.doi.org/10.1093/nar/gkp417
work_keys_str_mv AT sunjing seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT wudi seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT xutianlei seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT wangxiaojing seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT xuxiaolian seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT taolin seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT liyx seppaacomputationalserverforspatialepitopepredictionofproteinantigens
AT caozw seppaacomputationalserverforspatialepitopepredictionofproteinantigens