Reverse vaccinology approach to identify novel and immunogenic targets against Porphyromonas gingivalis: An in silico study

Abstract

Porphyromonas gingivalis is a primary causative agent of chronic periodontitis. Moreover, it leads to several systemic diseases, including rheumatoid arthritis, cardiovascular, neurodegenerative, and Alzheimer's diseases. It seems that the development of a vaccine against this bacterium is necessary. Thus, this study decided to identify novel immunogenic targets and developed multiple epitope-based vaccines against P. gingivalis. For this purpose, the pan/core-proteome of this bacterium was studied, and the suitable vaccine targets were selected based on different properties, including exposed localization of proteins, antigenicity, non-allergenicity, non-similarity to host proteome, stability, B-cell epitopes and MHC II binding sites, sequence conservation, molecular docking, and immune simulation. Through the quartile scoring method, 12 proteins with ≥ 20 scores were considered as suitable immunogenic targets. The results of the protein domain and functional class search showed that most of the immunogenic proteins were involved in the transport and metabolism of inorganic ions and lipids. In addition, two unknown function proteins, including WP_004584259.1 and WP_099780539.1 were detected as immunogenic targets. Three constructions carrying multi-epitopes were generated including Naked, LCL, and as chimeric structures. Among them, FliC chimeric protein had the strongest affinity to the human TLR2, 4, and 6, while the LCL platform represented the highest level of immune stimulation response. The obtained results from this study revealed new insights into prophylactic routes against P. gingivalis by introducing novel immunogenic targets. However, further investigations, including site-directed mutation and immunoassay are needed to confirm the pathogenic role and protectivity of these novel proteins.

Fig 1. Schematic representation of the selection and validation of novel putative immunogenic targets against P. gingivalis using a reverse vaccinology approach.

All criteria and thresholds are shown in the flowchart. MEVs: Multi-epitope vaccines.

Fig 2

A) Demonstration of the core-pan plot between 17 P. gingivalis strains with a cut-off > 0.5. The pan and core-proteome consist of 1985 and 1418 proteins, respectively. The X-axis of this figure shows the number of strains, and the Y-axis of Fig 2A shows the percentage of core- and pan- genes among strains. Blue triangles: number of total gene families. Pink triangles: number of core gene families. B) The distribution of the core, accessory and unique proteins among metabolic pathways was compared using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The X-axis shows different categories of proteins, and the Y-axis shows the percentage (proportion) of proteins in each category. The majority of core proteins were involved in metabolism, followed by genetic information processing, and environmental information processing.

Fig 3. Surface-exposed conformational epitopes of prioritized proteins.

The tertiary structures of the proteins were predicted by the Robetta web tool, and the surface-exposed epitopes were characterized on the 3D structure of proteins using Jmol software. The number of surface-exposed conformational epitopes of each protein is listed in parentheses: WP_099780539.1 (7 epitopes), WP_021664214.1 (6 epitopes), WP_099840460.1 (5 epitopes), WP_012457596.1 (5 epitopes), WP_005874477.1 (5 epitopes), WP_004583657.1 (5 epitopes), WP_004584259.1 (4 epitopes), WP_004583425.1 (4 epitopes), WP_099779133.1 (3 epitopes), WP_097626800.1 (2 epitopes), WP_004585254.1 (2 epitopes), and WP_211599956.1 (2 epitopes).

Fig 4. The comparative analysis of putative immunogenic targets against P. gingivalis based on quartile scoring method.

Twelve proteins with a score ≥ 20 were selected: WP_005874477.1 (26), WP_004583657.1 (25), WP_021664214.1 (25), WP_099840460.1 (25), WP_004585254.1 (23), WP_012457596.1 (23), WP_099779133.1 (23), WP_099780539.1 (23), WP_004583425.1 (21), WP_097626800.1 (21), WP_211599956.1 (21), and WP_004584259.1 (20).

Fig 5. Protein-protein interaction networks of two hypothetical proteins (WP_099780539.1 and WP_004584259.1) with unknown functions with other proteins of P. gingivalis.

WP_004584259.1 has neighborhood and co-occurrence interactions with TonB-dependent receptor (HR09_06515) and lipoproteins (HR09_06520).

Fig 6. The tertiary structure of multi-epitope vaccines and immune simulation.

A) 3D structures of the multi-epitope vaccines were predicted by the Robetta webtool and validated by the ProSA-web server. The linear epitopes are colored using Jmol software. B) The immunoreactivity of multiple epitope-based proteins was predicted by the C-ImmSim web server. 1. The levels of the B-cell population secreting IgM, IgG1 and IgG2. 2. The levels of Th1 populations. 3. The levels of IL-2 and IFN-γ cytokines. The LCL chimeric protein showed the greatest immunoreactivity.