A computational approach to developing a multi-epitope vaccine for combating Pseudomonas aeruginosa-induced pneumonia and sepsis

Abstract

Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine's composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa-related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model's authenticity once the protein's three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.

Keywords: OprE and OprF; Pseudomonas aeruginosa; immunoinformatics; multi-epitope vaccine.

Figure 1

(A) The multi-epitope vaccine possesses 407 amino acids. The N-terminal EAAAK linker includes the cholera toxin B buffer peptide. GPGPG linkers coupled B-cell, MHC-II, and MHC-I epitopes. (B) SOPMA secondary structure analysis revealed 21.87% alpha helices, 11.55% extended strands, 6.14% beta twists, and 60.44% random coils in the vaccine. (C) PSIPRED predicts vaccine construct solubility and secondary structure.

Figure 2

Vaccine 3D structure prediction, refinement, and validation. (A) The initial 3D structure of the vaccine (I-TASSER). (B) GalaxyRefine aided in refining the three-dimensional structure. (C) The residues in the Ramachandran plot were classified as 79.7% favourable, 18.4% acceptable, and 1.8% in the disallowed zone. (D) With an ERRAT quality aspect of 81.152%. (E) According to the ProSA-web evaluation, the E.Z. was −4.85.

Figure 3

HLA allele population coverage for particular epitopes by area and nation.

Figure 4

The ElliPro server predicts discontinuous B-cell epitopes for multi-epitope vaccination. 1–13. The green surface has discontinuous B-cell epitopes. 14. Discontinuous B-cell epitope residues and score.

Figure 5

(A) MHC-I receptor with a vaccine candidate interaction between chain A and chain B. (B) MHC-II receptor with vaccine candidate interaction between chain A and chain B and chain C. The interaction analysis was predicted by the web server (PDBsum) and virtualized by PyMOL.

Figure 6

Molecular dynamics simulation of a vaccine model with MHC-I includes several aspects. (A) Analysis of deformability through molecular dynamics simulations. (B) Examination of B-factors. (C) Evaluation of eigenvalues, where lower numbers indicate more facile deformation. (D) Analysis of variance, with red indicating individual variations and green indicating aggregate variances. (E) Covariance mapping, with red representing correlated regions, white demonstrating no correlation, and blue representing anti-correlation. (F) Elastic network analysis, where darker areas suggest increased stiffness.

Figure 7

Molecular dynamics simulation of a vaccine model with MHC-II includes several aspects. (A) Analysis of deformability through molecular dynamics simulations. (B) Examination of B-factors. (C) Evaluation of eigenvalues, where lower numbers indicate more facile deformation. (D) Analysis of variance, with red indicating individual variations and green indicating aggregate variances. (E) Covariance mapping, with red representing correlated regions, white demonstrating no correlation, and blue representing anti-correlation. (F) Elastic network analysis, where darker areas suggest increased stiffness.

Figure 8

In silico immune system simulations using the C-ImmSim service include the following observations: (A) Increases in IgM and IgG responses (depicted as a cream peak) and a reduction in antigen levels (black peak) were noted after the second and third injections. (B) Activation of the B-cell population is shown by a purple peak. (C) Boosting of B cells for memory functions is indicated by a green peak. (D) TH cell activation is represented by a purple peak. (E) The enhancement of memory TH cells is shown as a green peak. (F) The T-cell response showing Th1 polarization is depicted with a purple peak. (G) Increases in IL-2 (cream peak) and IFN-γ (purple peak) in response to the vaccine.

Figure 9

In silico pET-28a (+) vaccine cloning. The vector DNA was black, whereas the vaccination DNA was green. The SnapGene restriction sites EcoRI and EcoRV were cloned.