In silico construction of a multi-epitope vaccine (RGME-VAC/ATS-1) against the Rickettsia genus using immunoinformatics

Abstract

Background: Rickettsia is a genus of Gram-negative bacteria that causes various diseases, including epidemic typhus, Rocky Mountain spotted fever, and Mediterranean spotted fever. Ticks transmit these diseases and commonly found in developing regions with poor sanitation. As a result, it is difficult to estimate the number of these diseases cases, making it challenging to create prevention and diagnostic mechanisms.

Objectives: Thus, this study aimed to develop an in silico multi-epitope vaccine against Rickettsia.

Methods: Eight proteins were previously identified as potential vaccine candidates through reverse vaccinology and were screened for epitopes that bind to MHC class I and II molecules. The epitopes were then analysed for antigenicity, allergenicity, and toxicity. The selected epitopes were linked with AAY and GPGPG sequences peptide and a known adjuvant, the B-chain of Escherichia coli heat-labile enterotoxin, to form a chimeric multi-epitope protein. The protein's three-dimensional structure was predicted, and molecular docking analysis was performed against the toll-like receptor 4 (TLR4). Finally, the immune response to the protein was simulated using C-ImmSim tool.

Findings: A total of 26 immunogenic epitopes, formed the multi-epitope vaccine RGME-VAC/ATS-1. The vaccine showed excellent immunogenic parameters and was predicted to do not be toxic or allergenic to the host. It also showed good potential stimulation of immune cells, with a propensity to generate memory cells and elicit IFN-γ secretion.

Main conclusions: The in silico validations suggest that our study successfully designed an innovative multi-epitope vaccine against Rickettsia, addressing the challenges posed by the elusive nature of diseases caused by this genus. We provide a promising potential for further experimental exploration and the development of targeted prevention and diagnostic strategies for these diseases.

Fig. 1:. the image represents all selected epitopes interspersed with their respective linkers. Following the adjuvant (black), EAAAK adjuvant linker (dark blue), the epitopes of the helper t lymphocyte (HTL/B) complex (purple) interspersed with their GPGPG linker (yellow) are shown, followed by the epitopes of the cytotoxic T lymphocyte (CTL/B) complex (red) incorporated with their AAY ligands (green). The final structure comprises 538 amino acids.

Fig. 2:. results from the Psipred, ColabFold, GalaxyRefine, and SAVES programs for protein structures. In (A), the secondary structure predicted by Psipred is shown with an explanatory legend of the colour coding for each amino acid in the sequence. In (B), the top portion shows the 3D structure predicted by the ColabFold program and, below, the Ramachandran plot generated by the SAVES web service, with 63.1% of amino acids in favourable regions. In (C), the top portion shows the 3D structure of the protein refined by GalaxyRefine and, below, the Ramachandran plot generated by the SAVES web service, with 93% of amino acids in favourable regions.

Fig. 3:. molecular docking technique applied to the multi-epitope protein and toll-like receptor 4 (TLR4). In (A), the interaction generated by the ClusPro program is observed between the protein (on the left, predominantly light gray) and the TLR4 (on the right, mainly green). In (B), the visualisation of the interaction of residues participating in the molecular binding generated with the LigPlot+ program. In this case, hydrogen bonds are represented in green, semicircles below the dashed line in red represent hydrophobic interactions.

Fig. 4:. representative graphs of the molecular dynamics of the complex performed with the Gromacs program. The graph A evaluates the energy minimisation of the system, which was around 15 thousand kj/mol in around 700 ps. The graph B demonstrates that the system balanced at around 300k temperature in 10 ns. C graph demonstrates that the system balanced at around 1 bar pressure in 10 ns. The graph D represents the root mean square deviation (RMSD) of the complex (in red line) with a variation among 0,5 nm to 3 nm from the initial position) and the only TLR-4 alone (in black line) with a variation among 0,5 nm to 4 nm from the initial position). The graph E demonstrates the root mean square fluctuation (RMSF) of the interaction region complex (in red line) and the interaction region of TLR-4 alone (in black line) with a variation among 0.04 nm to 0.21 nm from the initial position.

Fig. 5:. in silico cloning of the multi-epitope construct. The optimised construct is depicted in red and inserted into the Escherichia coli pET28a(+) vector (in black) through the EagI and BamHI restriction enzymes.

Fig. 6:. graphs of the in silico immunological simulation generated using the C-Imm-Simm program about the multiepitope vaccine with adjuvant. In panel A, the simulation data for the populations of natural killer cells (NK), dendritic cells (DC), macrophages (MA), and epithelial cells (EP) are represented. In panel B, the simulation data for the populations of helper T cells (TH), regulatory T cells (Treg), and cytotoxic T cells (TC) are shown.

Fig. 7:. graphs of the in silico immunological simulation generated using the C-Imm-Simm program about the multiepitope vaccine without adjuvant. The simulation data for B cell populations and plasma B cells are depicted in panel A. The simulation data related to immunoglobulins and cytokines are shown in panel B.