A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family.


Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
01 May 2019
Historique:
entrez: 11 5 2019
pubmed: 11 5 2019
medline: 6 7 2019
Statut: epublish

Résumé

The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.

Sections du résumé

BACKGROUND BACKGROUND
The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.

Identifiants

pubmed: 31074372
doi: 10.1186/s12859-019-2736-2
pii: 10.1186/s12859-019-2736-2
pmc: PMC6509842
doi:

Substances chimiques

Epitopes 0
Viral Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

192

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Auteurs

Tao-Chuan Shih (TC)

Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.

Li-Ping Ho (LP)

Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan.

Jen-Leih Wu (JL)

Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.
Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.

Hsin-Yiu Chou (HY)

Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan. hychou@mail.ntou.edu.tw.
Department of Aquaculture, College of Life Science, National Taiwan Ocean University, Keelung, Taiwan. hychou@mail.ntou.edu.tw.

Tun-Wen Pai (TW)

Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@csie.ntut.edu.tw.
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan. twp@csie.ntut.edu.tw.

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