Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection
By Mudassar Ali, R. Pandey, N. Khatoon, A. Narula, Amit Mishra, V. Prajapati. Scientific Reports, 2017
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Dengue is considered as a major health issue which causes a number of deaths worldwide each year; tropical countries are majorly affected by dengue outbreaks. It is considered as life threatening issue because, since many decades not a single effective approach for treatment and prevention of dengue has been developed. Therefore, to find new preventive measure, we used immunoinformatics approaches to develop a multi-epitope based subunit vaccine for dengue which can generate various immune responses inside the host. Different B-cell, TC cell, and TH cell binding epitopes were predicted for structural and non-structural proteins of dengue virus. Final vaccine constructs consisting of TC and TH cell epitopes and an adjuvant (β-defensin) at N-terminal of the construct. Presence of B-cell and IFN-γ inducing epitopes confirms the humoral and cell mediated immune response developed by designed vaccine. Designed vaccine was not found allergic and was potentially antigenic in nature. Modeling of tertiary structure and the refined model was used for molecular docking with TLR-3 (immune receptor). Molecular docking and dynamics simulation confirms the microscopic interactions between ligand and receptor. In silico cloning approach was used to ensure the expression and translation efficiency of vaccine within an expression vector.
Subfields: Biotechnology, Molecular Biology, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/1d47946ef9eb4f5134c8f0d98c8c9fced572842a
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In Molecular Biology
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- GalaxyRefine2: simultaneous refinement of inaccurate local regions and overall protein structure (2019, methodology): “For instance, the server has been used by experimentalists in functional studies involving protein modelling to improve the quality of model structures obtained using other prediction methods (11–14).”