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In silico analysis and modeling of putative T cell epitopes for vaccine design of Toscana virus.

ABSTRACT: The sandfly fever Toscana virus is an important etiological agent known to cause human neurological infections in endemic Mediterranean countries during summer season. In the present study, prediction and modeling of T cell epitopes of Toscana virus (TOSV) antigenic proteins followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class II alleles were done. Immunoinformatics was applied in computational vaccinology to analyze the viral proteins which generate possible outcomes to elicit vaccine for TOSV. Here, immunoinformatic tool ProPred was used to predict the promiscuous MHC class II epitopes of viral antigenic proteins. The molecular modeling of the selected epitopes as well as MHC alleles was done at CPH model 3.2 server. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. The epitope/peptide VKMMIVLNL of viral nucleoprotein as well as VMILGLLSS of viral glycoprotein has shown the highest binding score with the same DRB1*1104 MHC II allele. These two predicted peptides are highly potential to induce T cell-mediated immune response and are expected to be useful in designing epitope-based vaccines after further testing. The results signify that the nucleoprotein, glycoprotein or the combination of both could be useful for future development of a vaccine controlling the spread of this emerging virus that could pose a new threat for humans.

SUBMITTER: Jain A

PROVIDER: S-EPMC4522722 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Reverse vaccinology is an outstanding strategy to identify antigens with high potential for vaccine development. Different parameters of five prediction programs were used to assess their sensitivity and specificity to identify B-cell epitopes of Chikungunya virus (CHIKV) strains reported in the IEDB database. The results, based on the use of 15 to 20 mer epitopes and the polyproteins to which they belong, were compared to establish the best parameters to optimize the prediction of antigenic peptides of the Mexican strain CHIKV AJV21562.1. LBtope showed the highest specificity when we used the reported epitopes and polyproteins but the worst sensitivity with polyproteins; ABCpred had similar specificity to LBtope only with the epitopes reported and showed moderate specificity when we used polyproteins for the predictions. Because LBtope was more reliable in predicting true epitopes, it was used as a reference program to predict and select six novel epitopes of the Mexican strain of CHIKV according to prediction frequency, viral genome localization, and non-homology with the human proteome. On the other hand, six bioinformatics programs were used with default parameters to predict T-cell epitopes in the CHIKV strains AJV21562.1 and AJV21561.1. The sequences of the polyproteins were analyzed to predict epitopes present in the more frequent HLA alleles of the Mexican population: DQA1*03011, DQA1*0401, DQA1*0501, DQB1*0201, DQB1*0301, DQB1*0302, and DQB1*0402. Fifteen predicted epitopes in the non-structural and 15 predicted epitopes in the structural polyprotein (9- to 16-mers) with the highest scores of each allele were compared to select epitopes with at least 80% identity. Next, the epitopes predicted with at least two programs were aligned to the human proteome, and 12 sequences without identity with the human proteome were identified as potential antigenic candidates. This strategy would be useful to evaluate vaccine candidates against other viral diseases affecting the countries of the Americas and to increase knowledge about these diseases.

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In silico analysis and modeling of putative T cell epitopes for vaccine design of Toscana virus.

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