Development of a Broad-Spectrum Pan-Mpox Vaccine via Immunoinformatic Approaches

Abstract

Monkeypox virus (MPXV) has caused 148,892 confirmed cases and 341 deaths from 137 countries worldwide, as reported by the World Health Organization (WHO), highlighting the urgent need for effective vaccines to prevent the spread of MPXV. Traditional vaccine development is low-throughput, expensive, time consuming, and susceptible to reversion to virulence. Alternatively, a reverse vaccinology approach offers a rapid, efficient, and safer alternative for MPXV vaccine design. Here, MPXV proteins associated with viral infection were analyzed for immunogenic epitopes to design multi-epitope vaccines based on B-cell, CD4+, and CD8+ epitopes. Epitopes were selected based on allergenicity, antigenicity, and toxicity parameters. The prioritized epitopes were then combined via peptide linkers and N-terminally fused to various protein adjuvants, including PADRE, beta-defensin 3, 50S ribosomal protein L7/12, RS-09, and the cholera toxin B subunit (CTB). All vaccine constructs were computationally validated for physicochemical properties, antigenicity, allergenicity, safety, solubility, and structural stability. The three-dimensional structure of the selected construct was also predicted. Moreover, molecular docking and molecular dynamics (MD) simulations between the vaccine and the TLR-4 immune receptor demonstrated a strong and stable interaction. The vaccine construct was codon-optimized for high expression in the E. coli and was finally cloned in silico into the pET21a (+) vector. Collectively, these results could represent innovative tools for vaccine formulation against MPXV and be transformative for other infectious diseases.

Keywords: Mpox; broad-spectrum vaccine; immunoinformatics approach; molecular docking; molecular dynamics (MD) simulations.

Figure A1

Prediction of transmembrane regions for A14L protein.

Figure A2

Prediction of transmembrane regions for A15L protein.

Figure A3

Prediction of transmembrane regions for A15.5L protein.

Figure A4

Prediction of transmembrane regions for A40L protein.

Figure A5

Prediction of transmembrane regions for B6R protein.

Figure A6

Prediction of transmembrane regions for B21R protein.

Figure A7

Prediction of transmembrane regions for H3L protein.

Figure A8

Prediction of transmembrane regions for L5L protein.

Figure A9

Prediction of transmembrane regions for M1R protein.

Figure A10

Prediction of transmembrane regions for M5R protein.

Figure A11

The optimized codons, codon adaptation index (CAI), and GC content.

Figure 1

Schematic workflow for designing a broad-spectrum, multi-epitope pan-Mpox vaccine.

Figure 2

Worldwide population coverage of selected CTL and HTL epitopes for vaccine development. Population coverage analysis of the selected CTL and HTL epitopes was performed using the IEDB tool, based on the distribution of HLA class I and II alleles.

Figure 3

Schematic illustration of the multi-epitope MPXV vaccine constructs consisting of adjuvant, CTL, HTL, and LBL epitopes; a 6xHis tag; and linkers. Adjuvant, CTL, HTL, LBL, and the 6xHis tag are represented by dark green, light blue, green, purple, and pink, respectively.

Figure 4

Secondary structures of the designed PADRE vaccine analyzed with the PSIPRED 4.0 server.

Figure 5

The final 3D structure of the PADRE-Mpox vaccine, predicted using AlphaFold 3 and subsequently refined with the GalaxyWEB server, is illustrated. In the visualization, different components are color-coded: PADRE adjuvant (red), EAAK linker (purple), CTL epitopes (blue), AAY linker (orange), HTL epitopes (magenta), GPGPG linker (grey), HEYGAEALERAG linker (cyan), LBL epitopes (green), KK linker (black), RVRR linker (yellow), and the 6xHis tag (brown).

Figure 6

Validation of the lead-refined model with the Ramachandran plot and z-score. (a) The Ramachandran plot of the selected refined model representing favored regions, allowed regions, generously allowed regions and disallowed regions. (b) Z-score generated by the ProSa-web server.

Figure 7

The graphs representing the immunization simulation by the C-IMMSIM server. (a–d) Levels of the B lymphocyte population after vaccination. (e,f) Helper T-cell population after vaccination. (g) Cytotoxic T-cell population after vaccination. (h) The levels of total NK cells, (i) total MA population per state, and (j) DC population per state. (k) Levels of cytokines and interleukin concentrations, with the inset plot indicating the danger signal together with leukocyte growth.

Figure 7

The graphs representing the immunization simulation by the C-IMMSIM server. (a–d) Levels of the B lymphocyte population after vaccination. (e,f) Helper T-cell population after vaccination. (g) Cytotoxic T-cell population after vaccination. (h) The levels of total NK cells, (i) total MA population per state, and (j) DC population per state. (k) Levels of cytokines and interleukin concentrations, with the inset plot indicating the danger signal together with leukocyte growth.

Figure 8

In silico cloning map showing the insertion of the vaccine sequence (shown in blue) into the pET21a (+) expression vector.

Figure 9

Graphs representing normal mode analysis (NMA) of the iMOD server. (a) The main-chain deformability. (b) B-factor graph (RMSD values). (c) Eigenvalue for structural deformation. (d) Individual variance and cumulative variance. (e) Atomic motion in the regions of MD. (f) Maps of the elastic network.

Figure 10

Graphs illustrating the results of molecular dynamics using GROMACS software. (a) RMSD, (b) RMSF, (c) Rg, (d) number of H bonds, and (e) the SASA.