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Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach.

ABSTRACT: Human coronavirus (HCoV), a member of Coronaviridae family, is the causative agent of upper respiratory tract infections and "atypical pneumonia". Despite severe epidemic outbreaks on several occasions and lack of antiviral drug, not much progress has been made with regard to an epitope-based vaccine designed for HCoV. In this study, a computational approach was adopted to identify a multiepitope vaccine candidate against this virus that could be suitable to trigger a significant immune response. Sequences of the spike proteins were collected from a protein database and analyzed with an in silico tool, to identify the most immunogenic protein. Both T cell immunity and B cell immunity were checked for the peptides to ensure that they had the capacity to induce both humoral and cell-mediated immunity. The peptide sequence from 88-94 amino acids and the sequence KSSTGFVYF were found as the most potential B cell and T cell epitopes, respectively. Furthermore, conservancy analysis was also done using in silico tools and showed a conservancy of 64.29% for all epitopes. The peptide sequence could interact with as many as 16 human leukocyte antigens (HLAs) and showed high cumulative population coverage, ranging from 75.68% to 90.73%. The epitope was further tested for binding against the HLA molecules, using in silico docking techniques, to verify the binding cleft epitope interaction. The allergenicity of the epitopes was also evaluated. This computational study of design of an epitope-based peptide vaccine against HCoVs allows us to determine novel peptide antigen targets in spike proteins on intuitive grounds, albeit the preliminary results thereof require validation by in vitro and in vivo experiments.

SUBMITTER: Oany AR

PROVIDER: S-EPMC4149408 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach.

Oany Arafat Rahman AR Emran Abdullah-Al AA Jyoti Tahmina Pervin TP

Drug design, development and therapy 20140821

Human coronavirus (HCoV), a member of Coronaviridae family, is the causative agent of upper respiratory tract infections and "atypical pneumonia". Despite severe epidemic outbreaks on several occasions and lack of antiviral drug, not much progress has been made with regard to an epitope-based vaccine designed for HCoV. In this study, a computational approach was adopted to identify a multiepitope vaccine candidate against this virus that could be suitable to trigger a significant immune response ...[more]

PMID: 25187696

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Project description:With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/in silico approaches, especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidates. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison and ranking for the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency (RMVP), MEBP vaccine potency (MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. Further, the MEBP variants with IDs, SPVC_446 and SPVC_537, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be preferred candidates for immediate experimental testing and validation. The method enables quicker selection and high throughput experimental validation of vaccine candidates. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time.

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.

Dataset Information

Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach.

Publications

Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach.

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