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An Immuno-informatics driven Epitope study from the molecular interaction of JEV non-structural (NS) proteins with Ribophorin (RPN)
Authors:Usman Sayeed  Gulshan Wadhwa  M Kalim A Khan  Qazi Mohd Sajid Jamal  Salman Akhtar  M Salman Khan
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
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.
Keywords:JEV  Non structural protein (NS)  Ribophorin (RPN)  B cell epitopes  homology modeling
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