Computational design and evaluation of a polyvalent vaccine for viral nervous necrosis (VNN) in fish to combat Betanodavirus infection

Abstract

Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine's efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.

Keywords: Betanodavirus; Aquaculture; Fish vaccine; Immunoinformatics; Molecular dynamics simulation; Multi-epitope vaccine; Viral nervous necrosis (VNN).

Fig. 1

Schematic and constructive representation of (A) vaccine construct V1 and (B) vaccine construct V2. Schematic representation illustrates CTL, HTL, LBL epitopes, and all linkers of the vaccines in bar format. Constructive representation shows the sequence of the vaccines.

Fig. 2

Structure prediction and validation of vaccine construct V1: (A) 3D model, (B) ERRAT quality value, (C) Ramachandran plot, (D) Z-score graph (overall quality), and (E) Z-score graph (sequence position); and of vaccine construct V2: (F) 3D model, (G) ERRAT quality value, (H) Ramachandran plot, (I) Z-score graph (overall quality), and (J) Z-score graph (sequence position).

Fig. 3

RMSD, RMSF and Rg analyses: (A) RMSD in C-α atoms of TLR7 and TLR8 bound to respective vaccine constructs, (B) RMSD in C-α atoms of vaccine constructs V1 and V2 bound to TLRs, (C) RMSF in side chain atoms of TLR7 bound to respective vaccine constructs, (D) RMSF in side chain atoms of TLR8 bound to respective vaccine constructs, (E) RMSF in side chain atoms of vaccine construct V1 bound to respective TLRs, and (F) RMSF in side chain atoms of vaccine construct V2 bound to respective TLRs. Radius of gyration analysis: (G) Rg in TLRs bound to respective vaccine constructs and (H) Rg in vaccine constructs bound to respective TLRs.

Fig. 4

Hydrogen bond analysis: (A) interchain hydrogen bonds between TLR7 and V1, and (B) Interchain hydrogen bonds between TLR7 and V2. (Surface view and cartoon representation showing interchain hydrogen bonds at different time intervals. TLR surface and cartoon in light blue, vaccine constructs in pink).

Fig. 5

Hydrogen bond analysis: (A) interchain hydrogen bonds between TLR8 and V1, and (B) Interchain hydrogen bonds between TLR8 and V2. (Same color schemes as Fig. 4 for surface view and cartoon representations).

Fig. 6

Gibbs free energy landscapes: (A) TLR7-V1 complex, (B) TLR8-V1 complex, (C) TLR7-V2 complex, and (D) TLR8-V2 complex.

Fig. 7

DCCM analysis: DCCM plots for (A) TLR7-V1 complex, (B) TLR8-V1 complex, (C) TLR7-V2 complex, and (D) TLR8-V2 complex.