Fig 1.
A. Complete step-by-step process of the methodology employed in the design of the vaccine against S. typhimurium.
B. Pipeline of the in silico immunoinformatics approach for multi-epitope vaccine design against S. typhimurium.
Table 1.
Prediction of the subcellular location of the selected proteins.
Table 2.
Details of the final shortlisted vaccine targets, including name, length, and UniProt ID, used in the vaccine design process.
Table 3.
List of CTL epitopes chosen for the vaccine design. The binding affinity values are reported as IC50 values in nanomolar (nM).
Table 4.
List of selected HTL epitopes for vaccine design.
Table 5.
List of B-cell epitopes used for the vaccine formulation.
Fig 2.
A. The ST-MEVC design is depicted as a sequence, with adjuvants represented in black, epitopes in yellow, green, and red, and linkers in black and blue.
B. The structural configuration of the several epitopes joined by linkers. The adjuvant is joined at the N-terminal, and a blue line represents the linkers for AAY, GPGPG, and EAAK.
Table 6.
Structural comparison of the initial and refined vaccine construct models generated by GalaxyRefine.
Fig 3.
Prediction and validation of the tertiary structure of ST-MEVC.
(A) The Swiss model of the vaccine’s three-dimensional structure. (B) ST-MEVC structure validation using ProSA-web, yielding a Z-score of −4.78. (C) PROCHECK’s Ramachandran plot explores 90.0% of the residues in the plot’s preferred area.
Fig 4.
3D representation of the docking sample.
(A) Three-dimensional depiction of the ST-MEVC docking complex utilizing human TLR4. (B, C) SS-MEVC chain A and TLR4 chain B molecules’ molecular connections.
Fig 5.
The previous vaccine design was replicated into the pET28a (+) expression vector via in silico restriction cloning. The red area represents the vaccine insert, while the black circle shows the vector.
Fig 6.
V chimeric peptide for in silico immunological activation, as forecasted by C-ImmSim Server A and B.
Antigen levels fall sharply as immunoglobulin antibody levels and B-cell populations rise(C). The maturation of B-cells in response to antigen stimulation. (D, E) The increase in the number of T-cytotoxic and T-helper cells is caused by repeated exposure to antigens. The vaccination phase was characterized by the rise in the population of macrophages, dendritic cells, and natural killer cells (F, G, H), (I), as a result of higher cytokine concentrations caused by repeated antigen exposure.
Fig 7.
The outcomes of the TLR4 complex and vaccine construct molecular dynamics simulations produced by the iMODS server.
NMA mobility in (A), deformability in (B), and the B-factor, which represents an averaged RMS, in (C). Eigenvalues in (D) (E) Colored bars display the cumulative (green) and individual (purple) variances; (F) The covariance matrix displays the paired residues correlated (blue), uncorrelated (white), and anti-correlated (red) movements. (G) In the model of elastic networks, stiffer parts are indicated by grey areas.
Fig 8.
Advanced molecular dynamics trajectory analysis of the ST-MEVC–TLR4 complex.
(A) Root Mean Square Deviation (RMSD) plot over 100 ns shows the system reaching equilibrium, with fluctuations stabilizing between 1.5–3.0 Å, indicating overall complex stability. (B) Root Mean Square Fluctuation (RMSF) analysis highlights residue-level flexibility, with pronounced mobility at the N- and C-termini and loop regions, while binding interface residues remain relatively rigid. (C–E) Principal Component Analysis (PCA) projections along PC1 vs. PC2 (C), PC1 vs. PC3 (D), and PC2 vs. PC3 (E) reveal dominant collective motions and conformational clustering, indicating restricted exploration of conformational space and a stable dynamic landscape. (F) Dynamic Cross-Correlation Matrix (DCCM) analysis illustrating positively correlated motions (blue) and anti-correlated motions (red) between residue pairs, supporting cooperative dynamics that underpin complex stability.