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Design of peptide epitope from the neuraminidase protein of influenza A and influenza B towards short peptide vaccine development.

ABSTRACT: Influenza viruses A and B are important human respiratory pathogens causing seasonal, endemic and pandemic infections in several parts of the globe with high morbidity and considerable mortality. The current inactivated and live attenuated vaccines are not effective. Therefore, it is of interest to design universal influenza virus vaccines with high efficacy. The peptide GQSVVSVKLAGNSSL of pandemic influenza, the peptide DKTSVTLAGNSSLCS of seasonal influenza and the peptide DILLKFSPTEITAPT of influenza B were identified as potential linear cell mediated epitopes. The epitopes predicted by BepiPred (B-cell epitope designer) program was subjected to docking experiment-using HexDock and CABS dock programs. The epitopes of pandemic H1N1 influenza A gave similar score of high affinity in docking. The epitope DKTSVTLAGNSSLCS of seasonal influenza A and epitope DILLKFSPTEITAPT of influenza B had high binding energy. It is further observed that the peptides GQSVVSVKLAGNSSL (pandemic influenza), DKTSVTLAGNSSLCS (seasonal influenza) DILLKFSPTEITAPT (influenza B) are found to interact with some known MHC class II alleles. These peptides have high-affinity binding with known MHC class II alleles. Thus, they have the potential to elicit cell immune response. These vaccines have to be further evaluated in animal models and human volunteers. These findings have application in the development of peptide B-cell epitope vaccines against influenza viruses.

SUBMITTER: Sankar S

PROVIDER: S-EPMC6077822 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Design of peptide epitope from the neuraminidase protein of influenza A and influenza B towards short peptide vaccine development.

Sankar Sathish S Ramamurthy Mageshbabu M Suganya Subramanian S Nandagopal Balaji B Sridharan Gopalan G

Bioinformation 20180531 5

Influenza viruses A and B are important human respiratory pathogens causing seasonal, endemic and pandemic infections in several parts of the globe with high morbidity and considerable mortality. The current inactivated and live attenuated vaccines are not effective. Therefore, it is of interest to design universal influenza virus vaccines with high efficacy. The peptide GQSVVSVKLAGNSSL of pandemic influenza, the peptide DKTSVTLAGNSSLCS of seasonal influenza and the peptide DILLKFSPTEITAPT of in ...[more]

PMID: 30108413

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Project description:ObjectiveInfluenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.MethodsThe present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.ResultsComputational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.ConclusionsWe propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.

Project description:BackgroundInfluenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.ResultsThis study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector.ConclusionsThe proposed "Recombinant multi-epitope vaccine" was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions.

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Design of peptide epitope from the neuraminidase protein of influenza A and influenza B towards short peptide vaccine development.

Publications

Design of peptide epitope from the neuraminidase protein of influenza A and influenza B towards short peptide vaccine development.

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