Molecular insights into pangenome localization and constructs design for Hemophilus influenza vaccine

Abstract

Haemophilus influenza, a major contributor to respiratory infections such as pneumonia, meningitis, sinusitis, chronic bronchitis, and acute otitis, poses a significant public health challenge, driven by rising antibiotic resistance particularly among the non-typeable H. influenza (NTHi) strains given their ability to evade immune surveillance. To address this, we employed a comprehensive immunoinformatics pipeline integrated with extensive pan-genome analysis of 59 strains of H. influenzae to design a novel multiepitope vaccine (MEV) candidate targeting most virulent and clinically significant proteins. Key surface exposed and virulence associated proteins, including Protein E, PilA, Protein D, P4, TolC, YadA, and HifC were prioritized based on their roles in bacterial adhesion, immune evasion, biofilm formation, and nutrient acquisition. Advanced in silico epitope prediction and verification strategies were utilized to map highly immunogenic regions across these proteins, followed by codon optimization to enhance expression efficiency in human systems. To further stabilize the vaccine construct, we performed disulfide engineering to enhance structural integrity and resilience. Comprehensive validation through in silico immune simulations, molecular dynamics (MD) simulations and binding free energy calculations confirmed the structural stability, immunogenic potential, and strong receptor affinity of the MEV candidate. Phylogenetic and virulence factor analysis further corroborated the broad coverage of the pathogenic relevance of the selected proteins. Together, our integrative approach presents a robust pipeline for rational vaccine design, offering a promising avenue toward combating multidrug resistant and immune evasive H. influenza strains.

Keywords: Haemophilus influenza; Immune simulations; Immunoinformatics; Molecular dynamics simulations; Multi-epitope vaccine.

Fig. 1

Complete flowchart of the study to identify the conserved vaccine candidates against the 59 H. influenzae strains.

Fig. 2

(A) The number of core gene families identified across 59 strains of H. influenzae is shown. The x-axis represents the genomes selected for the study, while the y-axis indicates the number of gene families present in each genome. (B) A graphical representation of the core and pan-genomes of 59 H. influenzae strains. The x-axis represents the number of strains, and the y-axis shows the number of genes in each strain. The black linear line illustrates pan-genome expansion with the addition of genomes, while the red linear line represents the stable and conserved core genome, which is critical for identifying vaccine targets as non-antigen and further subjected to the prediction of transmembrane helices by TMHMM and HMMTOP. Proteins having only one transmembrane helices are preferably chosen for the vaccine design as they are easily cloned and facilitate the expressions analysis. Finally, the filtered proteins were submitted to protein-protein interaction analysis to investigate the potential metabolic functional relationship between other proteins.

Fig. 3

The interactions predicted between hub-bottleneck proteins (shown in red) and their neighboring proteins were retrieved from the STRING online database.

Fig. 4

Disulfide Bond Engineering of HIF Vaccine Constructs (HIF-1, HIF-2, HIF-3, HIF-4, and HIF-5). The images display the original (left) and mutant (right) structures of each construct, highlighting the introduction of cysteine residues for disulfide bond formation.

Fig. 5

Predicted discontinuous B-cell epitopes mapped onto the 3D structures of the HIF vaccine constructs. (A) HIF-1, (B) HIF-2, (C) HIF-3, (D) HIF-4, and (E) HIF-5.

Fig. 6

In silico restriction cloning of the codon-optimized final vaccine constructs into the pET-28a (+) vector was carried out between the XhoI (158) and NdeI (1360) restriction enzyme sites using SnapGene software. The final constructs are designed for efficient production in E. coli (strain K12).

Fig. 7

Prediction of immune responses against the HIF-1 construct: (A) Total B-cell responses, (B) Total antibody responses, (C) Interleukin responses, (D) Total T-cell responses, (E) Macrophage activity, (F) Dendritic cell dynamics.

Fig. 8

The solid molecular surface representation of the predicted vaccines docked to the TLR-4 receptor.

Fig. 9

Visualization of the molecular interactions between the HIF-designed vaccine constructs (HIF-1, HIF-2, HIF-3, HIF-4, and HIF-5) and the TLR4 receptor.

Fig. 10

The graphs of Trajectory analysis that are representing the dynamic behaviour of HIF-1_TLR-4, HIF-2_TLR-4, HIF-3_TLR-4, HIF-4_TLR-4 and HIF-5_TLR-4 complexes throughout 500ns simulation. (A)RMSD graph, (B) RMSF graph, (C) Radius of gyration (Rg) graph and Beta-factor graph.

Fig. 11

Hydrogen bond Analysis of HIF vaccine constructs and the TLR-4 receptor. (A) HIF-1_TLR-4 complex, (B) HIF-2_TLR-4 complex, (C) HIF-3_TLR-4 complex, (D) HIF-4_TLR-4 complex, (E) HIF-5_TLR-4 complex.