A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family.
BMC Bioinformatics
; 20(Suppl 7): 192, 2019 May 01.
Article
en En
| MEDLINE
| ID: mdl-31074372
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Vertebrados
/
Proteínas Virales
/
Infecciones por Virus ADN
/
Interacciones Huésped-Patógeno
/
Invertebrados
/
Iridoviridae
/
Epítopos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Taiwán