Immunoinformatics-driven design of a multi-epitope vaccine targeting neonatal rotavirus with focus on outer capsid proteins VP4 and VP7 and non structural proteins NSP2 and NSP5

Abstract

Rotaviral gastroenteritis remains a major global health concern, particularly for infants and young children under five years old. Prior to the introduction of the WHO-prequalified rotavirus vaccine, rotavirus (RV) was responsible for approximately 800,000 child deaths annually in developing countries. Although vaccination efforts have reduced this number, RV still causes around 200,000 child deaths each year worldwide. The current WHO-prequalified vaccines are live attenuated and offer limited efficacy of 40-60%, with a slight risk of intussusception in young children. To overcome these limitations, we employed immunoinformatics to design a novel multi-epitope vaccine (MEV) targeting rotavirus outer capsid proteins VP4 and VP7, as well as crucial non-structural proteins NSP2 and NSP5. The RV-MEV incorporates 10 epitopes, including 4 CD8 + T-cell, 5 CD4 + T-cell, and 1 B-cell epitope, all of which are antigenic, non-allergenic, and non-toxic. These epitopes also showed potential to induce interferon-γ (IFN-γ). Molecular simulation studies confirmed stable interactions between RV-MEV and human TLR5 and integrin αvβ5 complexes. The RV-MEV was successfully cloned into a pET28a(+) vector during in-silico cloning. Immune simulation studies predict a strong immune response to the RV-MEV. Future in vitro and in vivo studies are necessary to validate the vaccine's effectiveness in providing protection against various rotavirus strains in neonates.

Keywords: B-Cell; Capsid protein; Gastroenteritis; Immune simulation; Immunoinformatics; Integrins; Molecular dynamics; Multi-epitope vaccine; Neonates; Non-structural proteins; Rotavirus; T-Cell; Toll-like receptors (TLRs).

Fig. 1

Computational workflow approach employed in the development of a multi-epitope vaccine for rotavirus, (i.e. RV-MEV).

Fig. 2

The RV-MEV elicits a response from the innate immune system through its interaction with Toll-like receptors (TLRs) and integrins located on the surface of B-cell, antigen presentation cells (APCs) such as dendritic cells and macrophages. The activation of Toll-like receptor 5 (TLR5) and Integrin αvβ5 can lead to the upregulation of major histocompatibility complex (MHC) molecules on antigen-presenting cells (APCs), therefore facilitating the engagement of CD8⁺ T-cells and CD4⁺ T-cells with their corresponding MHC-I and MHC-II receptors. It has been reported that human DC cross-presentation depends on the uptake of apoptotic material through CD36, αvβ3, and/or αvβ5 (Schulz et al., 2002). This interaction ultimately triggers the cellular immune response. In light of careful examination, the TLR5 receptor and Integrin αvβ5, which is anticipated to be the intended recipient of RV-MEV, guarantees the occurrence of entry into the dendritic cell and subsequently into the cytosol. This is a crucial stage in facilitating cross-presentation and the stimulation of CD8⁺ T-cells (Jafarpour et al., 2015). The interaction in question is anticipated to elicit a targeted immune response, resulting in the development of a memory cytotoxic T (T_c) cell capable of inducing cell lysis during an acute infection. In a similar way, the stimulation of B-cells results in the generation of memory B-cells.

Fig. 3

Schematic representation of the design of the anticipated RV-MEV construct, which incorporates several adjuvants, linkers, and epitopes, together with their corresponding amino acid sequences.

Fig. 4

(A) Final conformation of the TLR5 (green ribbon) and MEV (pink ribbon) complex obtained from the molecular docking simulation. The TLR5 model highlights key residues (pink spheres) that interact with the corresponding key residues of MEV (blue spheres). (B) Line plot showing the quality scores for both models, where TLR5 (gray line) exhibits good quality across the predicted structure, while MEV (yellow line) shows a lower score, attributed to the lack of structural information and the presence of highly flexible regions. (C) Node plot illustrating the interactions between TLR5 and MEV, indicating the specific residues involved and categorizing the interactions by type (e.g., hydrogen bonds, cationic, anionic).

Fig. 5

(A) Final configuration of the integrin (purple ribbon) and MEV (green ribbon) complex as predicted by the molecular docking simulation. The integrin structure highlights the critical residues (green spheres) interacting with the corresponding essential residues on the MEV (red spheres). (B) Line graph illustrating the quality scores for both models, where integrin (gray line) demonstrates reliable structural predictions, while the MEV (yellow line) shows lower scores due to limited structural data and highly flexible regions. (C) Network plot displaying the interactions between TLR5 and MEV, identifying the residues involved and specifying the types of interactions, including hydrogen bonding, cationic, and anionic interactions.

Fig. 6

(A) Dynamic behavior and Interactions of the TLR5-MEV Complex. This figure comprehensively illustrates the dynamic behavior of the TLR5-MEV complex over the simulation period. It includes RMSD (Root Mean Square Deviation) and RMSF (Root Mean Square Fluctuation) plots, which depict the stability and flexibility of the complex, respectively. The BSA (Buried Surface Area) plot shows the extent of the interaction interface between TLR5 and MEV, while the hydrogen bond plot indicates the consistency of hydrogen bonds over time. (B) Additionally, the figure features superimposed structural snapshots of the TLR5-MEV complex at 50 ns, 80 ns, 90 ns, and 100 ns, highlighting conformational changes.

Fig. 7

The panel (A) presents the result of MM-GBSA calculations and the delta energy generated for the interactions. The bar plot (B) detailing the key interactions between TLR5 and MEV, emphasizing the residues involved in maintaining the complex stability.

Fig. 8

Dynamics and Interactions of the Integrin-MEV Complex. This figure illustrates the dynamic behavior of the Integrin-MEV complex throughout the simulation. The RMSD (Root Mean Square Deviation) and RMSF (Root Mean Square Fluctuation) plots are included to show the complex’s stability and flexibility, respectively. The BSA (Buried Surface Area) plot depicts the interaction interface extent between Integrin and MEV, while the hydrogen bond plot demonstrates the stability of hydrogen bonds over the simulation period. Structural snapshots of the Integrin-MEV complex at 50 ns, 80 ns, 90 ns, and 100 ns are superimposed to highlight conformational changes.

Fig. 9

The first panel presents (A) the result of MM-GBSA calculations, and the delta energy generated for the interactions. The bar plot (B) detailing the key interactions between integrin and MEV, emphasizing the residues involved in maintaining the complex stability.

Fig. 10

In silico cloning of the RV-MEV construct into the pET28a (+) vector. The vaccine construct, which has been created, is depicted in the designated red region and will be replicated in the multiple cloning sites (MCS) together with 6 histidine residues at the C terminal.

Fig. 11

Immune simulation profile of the candidate RV-MEV showing (A) antigen count and antibody titer with Ig subclass; (B) B cell population; (C) T_h cell; (D) T_h population per state; (E) T_h cell count and percentage; (F) cytokines and interleukins.