Immunoinformatic Based Designing of Immune Boosting and Nonallergenic Multi-epitope Subunit Vaccine Against the Enterovirus D68.

Introduction: Enterovirus D68 (EV-D68) is a non-enveloped, positive-sense, singlestranded RNA virus known for causing severe respiratory illnesses and its association with acute flaccid myelitis (AFM) in children. Despite its increasing public health significance, no vaccines or antiviral drugs are currently available for EV-D68. This study aimed to design an immune-boosting multi-epitope subunit vaccine against EV-D68 using advanced immunoinformatic and machine learning approaches.

Methods: Capsid proteins VP1, VP2, and VP3 of EV-D68 were screened for immunogenic HTL, CTL, and B-cell epitopes to develop a non-allergenic, highly immunogenic multi-epitope vaccine. Predicted epitopes were subjected to 3D structural modeling and molecular dynamics simulations to validate folding and structural stability. Molecular docking and immune simulation techniques were employed to evaluate vaccine-TLR3 interactions and predict immune responses, respectively.

Results: Molecular docking analysis revealed strong binding affinities between the vaccine constructs and the TLR3 receptor, with scores of -299 kcal/mol, -361 kcal/mol, -258 kcal/mol, and -312 kcal/mol for VP1, VP2, VP3, and combined vaccine-TLR3 complexes. Molecular dynamic simulation and dissociation constant analyses confirmed the strength of these interactions, with binding free energies ranging from -57.75 kcal/mol to -101.35 kcal/mol. Codon adaptation index (CAI) values of 0.96 and GC content of ~69% supported the high expression potential of the vaccine constructs. Immune simulations demonstrated robust immune responses characterized by elevated IgG, IgM, cytokines, and interleukins, along with effective antigen clearance.

Discussion: The strong molecular interactions with TLR3 and simulated immune responses suggest that the designed vaccines can activate both innate and adaptive immunity. The high CAI and GC values support their expression feasibility in E. coli, enhancing prospects for production.

Conclusion: This study provides a strong foundation for the development of a safe and effective EV-D68 vaccine, showcasing the potential of computational vaccine design.