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dc.contributor.authorKaushik, Vikas
dc.contributor.authorG, Sunil Krishnan
dc.contributor.authorGupta, Lovi Raj
dc.contributor.authorKalra, Utkarsh
dc.contributor.authorShaikh, Abdul Rajjak
dc.contributor.authorCavallo, Luigi
dc.contributor.authorChawla, Mohit
dc.date.accessioned2022-06-22T06:35:32Z
dc.date.available2022-06-22T06:35:32Z
dc.date.issued2022-06-21
dc.identifier.citationKaushik, V., G, S. K., Gupta, L. R., Kalra, U., Shaikh, A. R., Cavallo, L., & Chawla, M. (2022). Immunoinformatics Aided Design and In-Vivo Validation of a Cross-Reactive Peptide Based Multi-Epitope Vaccine Targeting Multiple Serotypes of Dengue Virus. Frontiers in Immunology, 13. https://doi.org/10.3389/fimmu.2022.865180
dc.identifier.issn1664-3224
dc.identifier.doi10.3389/fimmu.2022.865180
dc.identifier.urihttp://hdl.handle.net/10754/679243
dc.description.abstractDengue virus (DENV) is an arboviral disease affecting more than 400 million people annually. Only a single vaccine formulation is available commercially and many others are still under clinical trials. Despite all the efforts in vaccine designing, the improvement in vaccine formulation against DENV is very much needed. In this study, we used a roboust immunoinformatics approach, targeting all the four serotypes of DENV to design a multi-epitope vaccine. A total of 13501 MHC II binding CD4+ epitope peptides were predicted from polyprotein sequences of four dengue virus serotypes. Among them, ten conserved epitope peptides that were interferon-inducing were selected and found to be conserved among all the four dengue serotypes. The vaccine was formulated using antigenic, non-toxic and conserved multi epitopes discovered in the in-silico study. Further, the molecular docking and molecular dynamics predicted stable interactions between predicted vaccine and immune receptor, TLR-5. Finally, one of the mapped epitope peptides was synthesized for the validation of antigenicity and antibody production ability where the in-vivo tests on rabbit model was conducted. Our in-vivo analysis clearly indicate that the imunogen designed in this study could stimulate the production of antibodies which further suggest that the vaccine designed possesses good immunogenicity.
dc.description.sponsorshipThis research received no external funding. The APC charges was funded by KAUST baseline research funding (to LC). The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. Authors would also like to acknowledge team members from STEMskills Research and Education Lab Private Limited for critical reading of manuscript and computational support.
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fimmu.2022.865180/full
dc.rightsArchived with thanks to Frontiers in Immunology under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleImmunoinformatics Aided Design and In-Vivo Validation of a Cross-Reactive Peptide Based Multi-Epitope Vaccine Targeting Multiple Serotypes of Dengue Virus
dc.typeArticle
dc.contributor.departmentKaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
dc.contributor.departmentChemical Science Program
dc.contributor.departmentKAUST Catalysis Center (KCC)
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalFrontiers in Immunology
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDomain of Bioinformatics, School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Punjab, India
dc.contributor.institutionDepartment of Research and Innovation, STEMskills Research and Education Lab Private Limited, Faridabad, India
dc.contributor.institutionDepartment of Data Science, Innopolis University, Innopolis, Russia
dc.identifier.volume13
kaust.personCavallo, Luigi
kaust.personChawla, Mohit
refterms.dateFOA2022-06-22T06:42:11Z
kaust.acknowledged.supportUnitBaseline research
kaust.acknowledged.supportUnitSupercomputing Laboratory


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Archived with thanks to Frontiers in Immunology under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as Archived with thanks to Frontiers in Immunology under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/