A Computational Approach for Identification of Epitopes in Dengue Virus Envelope Protein: A Step Towards Designing a Universal Dengue Vaccine Targeting Endemic Regions

Abstract

A major problem in designing vaccine for the dengue virus has been the high antigenic variability in the envelope protein of different virus strains. In this study, a computational approach was adopted to identify a multi-epitope vaccine candidate against dengue virus that may be suitable for large populations in the dengue-endemic regions. Different bioinformatics tools were exploited that helped the identification of a conserved immunological hot-spot in the dengue envelope protein. The tools also rendered the prediction of immunogenicity and population coverage to the proposed 'in silico' vaccine candidate against dengue. A peptide region, spanning 19 amino acids, was identified in the envelope protein which found to be conserved in all four types of dengue viruses. Ten proteasomal cleavage sites were identified within the 19-mer conserved peptide sequence and a total of 8 overlapping putative cytotoxic T cell (CTL) epitopes were identified. The immunogenicity of these epitopes was evaluated in terms of their binding affinities to and dissociation half-time from respective human leukocyte antigen (HLA) molecules. The HLA allele frequencies were studied among populations in the dengue endemic regions and compared with respect to HLA restriction patterns of the overlapping epitopes. The cumulative population coverage for these epitopes as vaccine candidates was high ranging from approximately 80% to 92%. Structural analysis suggested that a 9-mer epitope fitted well into the peptide-binding groove of HLA-A*0201. In conclusion, the 19-mer epitope cluster was shown to have the potential for use as a vaccine candidate against dengue.

A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment

“…It was found that YYYELYPTM interacts with 19 HLA-A, HLA-B and HLA-C alleles in total. Analyzing similar data from other study showed this specific high affinity binding is absolutely desired because the efficiency of an epitope vaccine greatly relies on the precise interaction between epitope and HLA alleles (Chakraborty et al, 2010). This specific high affinity binding is absolutely desired because the efficiency of an epitope vaccine greatly relies on the precise interaction between epitope and HLA alleles.…”

“…But novel vaccine approaches like DNA vaccines and epitope based vaccines have the potential to overcome these barriers for these vaccines can create more effective, specific, strong and long lasting immune response with minimal structure and devoid of all the undesired effects (Arnon, 2006). On top of that neutralizing peptide based vaccine design has been successfully suggested by using in silico approach against rhinovirus (Lapelosa et al, 2009) dengue virus (Chakraborty et al, 2010), Human coronaviruses (Sharmin and Islam, 2014), Hepatitis C virus (Idrees and Ashfaq, 2013), Saint Louis encephalitis virus (Hasan et al, 2013). Such studies regarding those viruses established immunoinformatics study.…”

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

“…Thus, using various bioinformatics tools, we can design peptide vaccines based on a neutralizing epitope. For example, in silico design of an epitope-based vaccine against human immunodeficiency virus 23,24 , coronavirus 25 , dengue virus 26 , and Saint Louis encephalitis virus 27 has already been reported.…”

“…In L1 protein, L1 [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] (EATVYLPPVPVSKVV-type16), L1 408-421 (PPPGGTLEDTYRFV-type16) and L1 404-417 (NFGVPPPPTTSLVD-type 18) epitopes had the best B cell epitope identification scores. For L2 protein, L2 [22][23][24][25][26][27][28][29][30][31][32][33][34][35] (KQSGTCPPDVVPKV-type18), L2 100-113 (PSDPSIVSLVEETS-type16), L2 94-107 (EPVGPTDPSIVTLI-type18) and L2 [57][58][59][60][61][62][63][64][65][66][67][68][69][70] (GLGIGTGSGTGGRT-type16) epitopes showed the highest epitope identification score between their own protein sequences.…”

Development of HPV16,18,31,45 E5 and E7 peptides-based vaccines predicted by immunoinformatics tools

“…, A:Y47, A:V50, A:F51, A:I52, A:I54, A:P55, A:L56, A:F57, A:V58, A:I59, A:H60, A:T61, A:H62, AHigher rates show better quality of epitope identificationKhairkhah et al 2018), dengue virus(Chakraborty et al 2010) and coronavirus(Oany et al 2014). …”