Unknown

Dataset Information

0

In Silico Identification of Highly Conserved Epitopes of Influenza A H1N1, H2N2, H3N2, and H5N1 with Diagnostic and Vaccination Potential.


ABSTRACT: The unpredictable, evolutionary nature of the influenza A virus (IAV) is the primary problem when generating a vaccine and when designing diagnostic strategies; thus, it is necessary to determine the constant regions in viral proteins. In this study, we completed an in silico analysis of the reported epitopes of the 4 IAV proteins that are antigenically most significant (HA, NA, NP, and M2) in the 3 strains with the greatest world circulation in the last century (H1N1, H2N2, and H3N2) and in one of the main aviary subtypes responsible for zoonosis (H5N1). For this purpose, the HMMER program was used to align 3,016 epitopes reported in the Immune Epitope Database and Analysis Resource (IEDB) and distributed in 34,294 stored sequences in the Pfam database. Eighteen epitopes were identified: 8 in HA, 5 in NA, 3 in NP, and 2 in M2. These epitopes have remained constant since they were first identified (~91 years) and are present in strains that have circulated on 5 continents. These sites could be targets for vaccination design strategies based on epitopes and/or as markers in the implementation of diagnostic techniques.

SUBMITTER: Munoz-Medina JE 

PROVIDER: S-EPMC4544958 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

altmetric image

Publications


The unpredictable, evolutionary nature of the influenza A virus (IAV) is the primary problem when generating a vaccine and when designing diagnostic strategies; thus, it is necessary to determine the constant regions in viral proteins. In this study, we completed an in silico analysis of the reported epitopes of the 4 IAV proteins that are antigenically most significant (HA, NA, NP, and M2) in the 3 strains with the greatest world circulation in the last century (H1N1, H2N2, and H3N2) and in one  ...[more]

Similar Datasets

| S-EPMC3754050 | biostudies-literature
| S-EPMC3957958 | biostudies-literature
| S-EPMC3414522 | biostudies-literature
2017-05-05 | GSE98527 | GEO
| S-EPMC3720064 | biostudies-literature
| S-EPMC5572660 | biostudies-other
| S-EPMC2836360 | biostudies-literature
2016-10-27 | GSE89008 | GEO
| S-EPMC3538841 | biostudies-literature
2017-05-05 | GSE97672 | GEO