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      Immunoinformatics Design of a Novel Multi-Epitope Peptide Vaccine to Combat Multi-Drug Resistant Infections caused by Vibrio vulnificus

      , , ,
      European Journal of Pharmaceutical Sciences
      Elsevier BV

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          Abstract

          <p class="first" id="d3584813e77">Multi-drug resistant Vibrio vulnificus is a Gram-negative bacillus responsible for diseases, such as: sepsis, septicemia, gastroenteritis, and fatal necrotizing fasciitis in humans. The treatment and prevention of V. vulnificus infections are challenging because of resistance to antibiotics and the non-availability of a licensed vaccine. Considering this, an in-silico based approach comprising subtractive proteomics, immunoinformatics, molecular docking, and dynamics simulation studies is applied herein to identify potential epitope vaccine candidates for the mentioned pathogen. Two potential vaccine candidates: vibC and flgL are filtered based on essentiality, outer membrane localization, virulence, antigenic, pathway mapping, and cellular protein-protein network analysis. Using immunoinformatic tools, 9-mer B-cell derived T-cell antigenic epitopes are predicted for the said shortlisted two proteins that are demonstrating excellent affinity for predominant HLA allele (DRB1*0101) in human population. Screened peptides are used further in multi-epitope peptide designing and linked to an adjuvant to enhance the immunogenic properties of the designed construct. Furthermore, the construct was docked blindly to TLR4 immune receptor, and analyzed in conformational dynamics simulation to decipher the complex affinity and understand time dependent behavior, respectively. We expect this designed in silico construct to be useful for vaccinologists to evaluate its immune protective efficacy in in vivo animal models. </p>

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          Author and article information

          Journal
          European Journal of Pharmaceutical Sciences
          European Journal of Pharmaceutical Sciences
          Elsevier BV
          09280987
          November 2019
          November 2019
          : 105160
          Article
          10.1016/j.ejps.2019.105160
          31751777
          4c1ed82d-38fe-42c3-90ef-9d2dd1d1e88a
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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