In silico strategies for predicting therapeutic peptides targeting the capsid protein of the dengue virus

The production of vaccines has been significantly advanced by the availability of immunological data and the developments in reverse vaccinology. In this study, we focus on the structural capsid proteins of the dengue virus to predict cell-binding epitopes, aiming to create an epitope-based dengue vaccine using immunoinformatics. We utilize in silico techniques to identify B cell, HTL, and CTL epitopes, and assess the developed vaccine's antigenicity, allergenicity, toxicity, and physicochemical characteristics with tools, such as VaxiJen, AllerTOP, ToxinPred, and ExpasyProtParam. The 3D structures of the epitopes are predicted, refined, and validated using PEPstr. Docking and molecular simulation are performed using HPEPDOCK and Gromacs. Molecular docking analysis identified optimal epitopes with binding affinity scores of −142.218, −145.733, −138.460, and −174.477 kcal/mol for KSKAINVLR, YCIEAKLTN, IKKSKAINV, and FNMLKRERN, respectively. Molecular dynamic simulation studies also revealed the stability of the selected peptides over the period of 100 ns with a good population conservancy analysis. Therefore, KSKAINVLR, YCIEAKLTN, IKKSKAINV, and FNMLKRERN are suggested as the best epitopes to elicit immunity against the dengue virus. The identified epitopes from the capsid protein offer promising candidates for novel anti-dengue treatments. Based on these findings, these designed vaccine candidates should undergo further experimental research and in vivo testing to confirm their efficacy. The results demonstrate the high quality of the proposed vaccine.