Epitope-Based Peptides Prediction from Proteome of Nipah Virus

Abstract

The identification of MHC class II epitope-based peptides are urgently needed for appropriate vaccination against Nipah virus (NiV) because there are currently no approved vaccines for human NiV infection. In the present study, prediction and modeling of T cell epitopes of NiV antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scores with their corresponding MHC class II alleles were done. Immunoinformatic tool ProPred was used to predict the promiscuous MHC class II epitopes of viral antigenic proteins. PEPstr server did the 3D structure models of the epitopes and Modeller 9.10 did alleles. We docked epitope with allele structure using the AutoDock 4.2 Tool. The docked peptide–allele complex structure was optimized using molecular dynamics simulation for 5 ps with the CHARMM-22 force field using NAnoscale Molecular Dynamics program incorporated in visual molecular dynamics (VMD 1.9.2) and then evaluating the stability of complex structure by calculating RMSD values. Epitope MKLQFSLGS of Matrix protein has considerable binding energy and score with DRBI*0421 MHC class II allele. This predicted peptide has potential to induce T cell-mediated immune response and is expected to useful in designing epitope-based vaccines against NiV after further testing by wet lab studies.