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. 2021 Oct 5;11(1):19707.
doi: 10.1038/s41598-021-99227-7.

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Adewale Oluwaseun Fadaka  1 , Nicole Remaliah Samantha Sibuyi  2 , Darius Riziki Martin  2 , Mediline Goboza  2 , Ashwil Klein  3 , Abram Madimabe Madiehe  2   4 , Mervin Meyer  5
Affiliations

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Adewale Oluwaseun Fadaka et al. Sci Rep. .

Abstract

Dengue poses a global health threat, which will persist without therapeutic intervention. Immunity induced by exposure to one serotype does not confer long-term protection against secondary infection with other serotypes and is potentially capable of enhancing this infection. Although vaccination is believed to induce durable and protective responses against all the dengue virus (DENV) serotypes in order to reduce the burden posed by this virus, the development of a safe and efficacious vaccine remains a challenge. Immunoinformatics and computational vaccinology have been utilized in studies of infectious diseases to provide insight into the host-pathogen interactions thus justifying their use in vaccine development. Since vaccination is the best bet to reduce the burden posed by DENV, this study is aimed at developing a multi-epitope based vaccines for dengue control. Combined approaches of reverse vaccinology and immunoinformatics were utilized to design multi-epitope based vaccine from the sequence of DENV. Specifically, BCPreds and IEDB servers were used to predict the B-cell and T-cell epitopes, respectively. Molecular docking was carried out using Schrödinger, PATCHDOCK and FIREDOCK. Codon optimization and in silico cloning were done using JCAT and SnapGene respectively. Finally, the efficiency and stability of the designed vaccines were assessed by an in silico immune simulation and molecular dynamic simulation, respectively. The predicted epitopes were prioritized using in-house criteria. Four candidate vaccines (DV-1-4) were designed using suitable adjuvant and linkers in addition to the shortlisted epitopes. The binding interactions of these vaccines against the receptors TLR-2, TLR-4, MHC-1 and MHC-2 show that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, DV-1 has a better binding energies of - 60.07, - 63.40, - 69.89 kcal/mol against MHC-1, TLR-2, and TLR-4, with respect to the other vaccines. All the designed vaccines were highly antigenic, soluble, non-allergenic, non-toxic, flexible, and topologically assessable. The immune simulation analysis showed that DV-1 may elicit specific immune response against dengue virus. Moreover, codon optimization and in silico cloning validated the expressions of all the designed vaccines in E. coli. Finally, the molecular dynamic study shows that DV-1 is stable with minimum RMSF against TLR4. Immunoinformatics tools are now applied to screen genomes of interest for possible vaccine target. The designed vaccine candidates may be further experimentally investigated as potential vaccines capable of providing definitive preventive measure against dengue virus infection.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The prepared 3D structures of the Toll-like and Major histocompatibility complex receptors used in this study. (A) TLR-2; PDB ID: 2ZX; (B) TLR-4; PDB ID: 3FX1; (C) MHC class I; PDB ID: 5WJL; and (D) MHC class II; PDB ID: 5JLZ.
Figure 2
Figure 2
The secondary structures of the predicted dengue virus vaccines (PSIPRED).
Figure 3
Figure 3
The predicted multi-epitope vaccines for DENV. A) DV-1; B) DV-2; C) DV-3; and (D) DV-4 as modelled by Schrödinger.
Figure 4
Figure 4
Validation of the modeled-predicted DENV vaccines with ProSA-Web.
Figure 5
Figure 5
Docking poses of the predicted DENV vaccines against selected immunological receptors (Schrodinger suite v2020-3).
Figure 6
Figure 6
The solid molecular surface display of the predicted vaccines docked to the MHC class-2 receptor.
Figure 7
Figure 7
The solid molecular surface display of the predicted vaccines docked to the TLR-4 receptor.
Figure 8
Figure 8
The linear vaccine constructs with XhoI and NdeI sites. (A) DV-1, (B) DV-2, (C) DV-3, and (D) DV-4 (SnapGene software).
Figure 9
Figure 9
In silico restriction cloning of the codon optimized final designed vaccines in the pET-28a (+) vector between the XhoI (158) and NdeI (238) restriction enzyme sites (SnapGene software). The final constructs can further be expressed in E coli (strain K12) for efficient vaccine production.
Figure 10
Figure 10
Immunogenic potential of the designed vaccine (DV-1). In response to the exposure of DV-1 after three injections, (A) production of immunoglobulins (B) active B-cell populations/state; (C) plasma B-lymphocytes and their isotypes per state; (D) helper T-cell population/state; (E) cytotoxic T-cell population/state; (F) reduction in the level of T regulatory cells; (G) dendritic cell population per state; (H) activity of macrophage population/state; (I) cytokine level and interleukins (smaller plot) in different states with the Simpson index (dotted line). All units are in cells/mm3 in three subsequent immune responses.
Figure 11
Figure 11
Molecular dynamics simulation of (DV-1-TLR-4 complex at 100 ns (ns). (A) Molecular dynamics trajectory (system set-up for the simulation), (B) Root Mean Square Deviation of docked complex shows very minimal deviation which reflects the stable microscopic interaction between DV1 and TLR4 molecule (C) RMSF-Root Mean Square Fluctuation plot of docked protein complex side chain fluctuation in plot generates peak which reflects the flexibility of side chain of docked protein complex. (D) Time evolution of the radius of gyration (rRyr) during 100 ns of MD simulation.
Figure 12
Figure 12
Overview of the study design.
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