Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications

Abstract

Tuberculosis is an airborne infectious disease caused by Mycobacterium tuberculosis. BCG is the only approved vaccine. However, it has limited global efficacy. Pathogens could affect the transcription of host genes, especially the ones related to the immune system, by inducing epigenetic modifications. Many proteins of M. tuberculosis were found to affect the host's epigenome. Nine proteins were exploited in this study to predict epitopes to develop an mRNA vaccine against tuberculosis. Many immunoinformatics tools were employed to construct this vaccine to elicit cellular and humoral immunity. We performed molecular docking between selected epitopes and their corresponding MHC alleles. Thirty epitopes, an adjuvant TLR4 agonist RpfE, constructs for subcellular trafficking, secretion booster, and specific linkers were combined to develop the vaccine. This proposed construct was tested to cover 99.38% of the population. Moreover, it was tested to be effective and safe. An in silico immune simulation of the vaccine was also performed to validate our hypothesis. It also underwent codon optimization to ensure mRNA's efficient translation once it reaches the cytosol of a human host. Furthermore, secondary and tertiary structures of the vaccine peptide were predicted and docked against TLR-4 and TLR-3.Molecular dynamics simulation was performed to validate the stability of the binding complex. It was found that this proposed construction can be a promising vaccine against tuberculosis. Hence, our proposed construct is ready for wet-lab experiments to approve its efficacy.

Figure 1

Visualization of the docking between the epitope HLGYKCSIRK and its corresponding MHC allele (HLA-A*03:01) using the PyMol software: (A) Surface View. (B) Cartoon View.

Figure 2

Different Interactions between the epitope and its corresponding MHC allele visualized using the discovery studio. (A) Conventional Hydrogen Bonds (B) Hydrophobic Interactions (C) Salt Bridge, attractive Charge interactions (D) Cation-Pi interactions (E) Donor-Donor Clash (F) Carbon Hydrogen Bond (G) Pi Donor Hydrogen Bond.

Figure 3

In Silico Immune Simulation against the mRNA vaccine retrieved from the C-ImmSim server. (https://kraken.iac.rm.cnr.it/C-IMMSIM/). (A) The immunoglobulin production after antigen injection. (B) The B cell population after three injections. (C) The B Cell Population per state (D) The Helper T Cell Population (E) The Helper T Cell Population per state (F) The Cytotoxic T Cell Population per state (G) Macrophage Population per state (H) Dendritic Cell Population per state (I) Cytokines and Interleukins Production with Simpson Index of the immune response.

Figure 4

Codon optimization and mRNA vaccine structure prediction: (A) CAI value (B) GC% (C) CFD value (D) Optimal secondary structure (E) Centroid secondary structure of the vaccine mRNA retrieved using RNAfold Webserver (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi).

Figure 5

Structure prediction and validation of the peptide vaccine construct: (A) The secondary structure of the vaccine using the PSIPRED server (B) Tertiary structure of the peptide using the Robetta server (C) Ramachandran plot analysis using the PROCHECK server (D) Z-score analysis using Pro-SA webserver.

Figure 6

The six predicted conformational B-cell epitopes using the ElliPro tool of the IEDB database: (I) 2D diagram of the positions of conformational B-cell epitopes. (II) The 3D models of B-cell epitopes. The yellow spheres represent the conformational B-cell epitopes. (A) 53 residues with a score of 0.809. (B) 140 residues with a score of 0.726. (C) 10 residues with a score of 0.699. (D) 120 residues with a score of 0.681. (E) 7 residues with a score of 0.582. (F) 5 residues with a score of 0.515.

Figure 7

Molecular dynamics simulation, Normal Mode Analysis, and receptor-ligand interactions: (A) Vaccine-TLR4 docked complex using the Cluspro server (B) Deformability graph (C) B-factor graph (D) Eigenvalue of vaccine-TLR4 complex (E) Covariance matrix (F) Elastic network model using the iMODS server (G) Receptor-ligand interaction using the PDBsum webserver.

Figure 8

Molecular dynamics simulation, Normal Mode Analysis, and receptor-ligand interactions: (A) Vaccine-TLR3 docked complex using the Cluspro server (B) Deformability graph (C) B-factor graph (D) Eigenvalue of vaccine-TLR3 complex (E) Covariance matrix (F) Elasticnetwork model using the iMODS server (G) Receptor-ligand interaction using the PDBsum webserver.

Figure 9

Proposed In vitro mechanism of production and In vivo method of delivery: (A) In vitro transcription of vaccine sequences (B) Vector-mediated delivery into the body and the mRNA transits to the cytosol (C) Mechanism of action of mRNA vaccine. Once it is translated into a protein in the cytosol, it undergoes PTMs and becomes a fully functional and properly folded protein. The tPA secretory signal and MITD sequences direct the peptides to specific compartments inside the cell (ER and Golgi apparatus) to either induce their secretion (LBL epitopes) or presentation (HTL and CTL epitopes) by the MHCI and MHCII.

Figure 10

Workflow of RABA_MARZ_14.5.9 mRNA vaccine Development.