Immunoinformatics Prediction of Peptide-Based Vaccine Against AfricanHorse Sickness Virus

Immunome Research(2017)

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摘要
Background: African horse sickness (AHS) viral disease of equidae. It transmitted by hematophagous Culicoides midges (Diptera, Ceratopogonidae) and causes severe disease in horse that can lead to death. African Sickness (AHSV) double-stranded RNA (dsRNA) virus with ten genome segments encoding seven structural proteins (VP1-VP7) and four non-structural proteins (NS1, NS2, NS3, NS3A). The aim of this study is to analyze (VP2) protein of the African Sickness (AHSV) strains reported in the National Center Biotechnology Information database (NCBI) database to select all possible epitopes that can be used to design a peptide vaccine.Materials and methods: A total of 27 outer capsid protein (VP2) sequences of African Sickness Virus (AHSV) retrieved from the National Center for Biotechnology Information database (NCBI) (https://www.ncbi.nlm. nih.gov/protein/?term=VP2+African+horse+sickness+virus) in the 7th of September 2016. On them, several tests were conducted using Immune Epitope Analysis Database (IEDB) to detect the highly conserved immunogenic epitopes of and T cells from which all possible epitopes that can be used as therapeutic peptide vaccine to be selected.Results and Discussion: Regarding epitopes that would elicit an antibody immune response, “FSPEYY, DKVVEDPESY and YDTDQNVV “were proposed to stimulate B cell. While 5 epitopes for each MHC I and II were addressed as potentially promising epitopes as bound the highest number of alleles, all these epitopes were found to have high binding affinity and the lowest binding energy to equine MHC class I molecule (ELA-A3) haplotype in the structural level. The epitopes “YAYCLILAL and YTFGNKFLL” because they bound to the largest number of alleles. In spite of binding to 4 alleles the epitope WFFDYYATL was represented because it has the lowest global energy. To our knowledge there no epitope based vaccine for the African Horse Sickness (AHSV) using in silico approaches.
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