An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Abstract

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Fig 1. An overview of the study.

Fig 2. The vaccine construct contains CTL (green color), HTL (orange color), and B-cell epitopes (blue color) with the linkers (adjacent line) and an adjuvant (olive color).

Fig 3. The secondary structure of the vaccine was predicted by the PSIPRED server.

Fig 4. The predicted tertiary structure of the vaccine construct by I-TASSER.

The ribbon (A) and surface (B) model view of the vaccine’s tertiary structure was visualized by PyMol software.

Fig 5

The Ramachandran plot and the Z-score of the predicted tertiary structure before (A and C) and after structural refinement (B and D).

Fig 6. The docked complex of “Vaccine—TLR-2” and their interacting amino acid residues predicted by the Cluspro 2.0 server.

Fig 7. The docked complex of “Vaccine—TLR-4” and their interacting amino acid residues predicted by the Cluspro 2.0 server.

Fig 8. The predicted discontinuous B-cell epitopes of the vaccine (A–I).

Yellow surfaces indicate the predicted discontinuous B-cell epitopes, while cyan sticks reveal the vaccine.

Fig 9. In silico cloning of the vaccine’s optimized codon sequences into pET28a (+) vector.

Restriction sites indicated in yellow boxes show the two restriction sites (Acc65I and PshAI).

Fig 10. Exploring the vaccine’s immune simulation using the C-ImmSim server.

The evolution of entire (A) and per state (B) B-cell populations, NK (C) and DC (D) cell populations, the population of Mφ per state (E), and the cytokines and the IL-2 level are illustrated by the primary plot and the sub-plot, respectively (D) (Here, D refers to Simson’s index, which measures the degree of variety. Since an increase in D suggests an increase in the number of epitope-specific T-cells, a lower D value indicates a lower level of diversity).

Fig 11. T-cells mediated immune responses predicted by the C-ImmSim server.

The evolution of Th with their memory cell life span (A), Th cell population per state cell (B), the development of entire Tc populations (C) and Tc population per state cell (D), the Treg populations per state (E), and the antigen and antibody titers after post vaccination state (F).

Fig 12. Predicted mRNA structure of the vaccine by RNAfold web server.

The base pair probabilities of the mRNA vaccine with the minimum free energy (A) and centroid (B) structure and the positional entropy of the mRNA vaccine with the minimum free energy (C) and centroid (D) structure.