An Immuno-informatics driven Epitope study from the molecular interaction of JEV non-structural (NS) proteins with Ribophorin (RPN) |
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Authors: | Usman Sayeed Gulshan Wadhwa M Kalim A Khan Qazi Mohd Sajid Jamal Salman Akhtar M Salman Khan |
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Institution: | 1.Department of Biosciences, Integral University, Lucknow-226026, India;2.Department of Biotechnology, Ministry of Science and Technology, CGO complex, Lodhi Road, New Delhi-110 003, India |
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Abstract: | Japanese encephalitis (JE) is an acute viral infection of the central nervous system where the JE virus infects the lumen of
the endoplasmic reticulum (ER) and rapidly accumulates substantial amount of seven different nonstructural proteins (NS). These
NS proteins tend to bind on a glycoprotein receptor, ribophorin (RPN) resulting in the malfunctioning of ER in host cells,
subsequently triggering an unfolded protein response. Therefore, it is of interest to predict the best possible antigenic determinants
in the NS protein capable of eliciting immune response as a strategy to combat JE. Hence, it is our interest to explore the most
potent NS protein among all showing the best possible molecular interaction with the RPN receptor present on ER. However, the
structures of these NS protein and RPN are currently unknown. Thus, we modeled their structures using the established homology
modeling techniques in the MODELLER 9v10 software. The molecular docking of NS proteins with RPN was subsequently
completed using the Discovery Studio 2.5 software suite. The docked conformations of RPN with NS were further analyzed and its
graphical interpretations were presented for identifying the most potential NS protein for efficient epitope activity. Further, the B
cell epitopes were mapped using BCPred and the predicted epitope regions are documented. The data presented in this report
provides useful insights towards the design and development of potential epitopes to generate a vaccine candidate against JEV. |
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Keywords: | JEV Non structural protein (NS) Ribophorin (RPN) B cell epitopes homology modeling |
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