Research Article: In silico analysis as a strategy to identify candidate epitopes with human IgG reactivity to study Porphyromonas gingivalis virulence factors

Date Published: March 11, 2019

Publisher: Springer Berlin Heidelberg

Author(s): Ellen Karla Nobre dos Santos-Lima, Kizzes Araújo Paiva Andrade Cardoso, Patrícia Mares de Miranda, Ana Carla Montino Pimentel, Paulo Cirino de Carvalho-Filho, Yuri Andrade de Oliveira, Lília Ferreira de Moura-Costa, Teresa Olczak, Isaac Suzart Gomes-Filho, Roberto José Meyer, Márcia Tosta Xavier, Soraya Castro Trindade.


Porphyromonas gingivalis (Pg) is one of the main pathogens in chronic periodontitis (CP). Studies on the immunogenicity of its virulence factors may contribute to understanding the host response to infection. The present study aimed to use in silico analysis as a tool to identify epitopes from Lys-gingipain (Kgp) and neuraminidase virulence factors of the Pg ATCC 33277 strain. Protein sequences were obtained from the NCBI Protein Database and they were scanned for amino acid patterns indicative of MHC II binding using the MHC-II Binding Predictions tool from the Immune Epitope Database (IEDB). Peptides from different regions of the proteins were chemically synthesized and tested by the indirect ELISA method to verify IgG immunoreactivity in serum of subjects with CP and without periodontitis (WP). T cell epitope prediction resulted in 16 peptide sequences from Kgp and 18 peptide sequences from neuraminidase. All tested Kgp peptides exhibited IgG immunoreactivity whereas tested neuraminidase peptides presented low IgG immunoreactivity. Thus, the IgG reactivity to Kgp protein could be reaffirmed and the low IgG reactivity to Pg neuraminidase could be suggested. The novel peptide epitopes from Pg were useful to evaluate its immunoreactivity based on the IgG-mediated host response. In silico analysis was useful for preselecting epitopes for immune response studies in CP.

Partial Text

Chronic periodontitis is a multifactorial and polymicrobial disease, which may negatively influence systemic diseases (Hajishengallis 2015). Its pathogenesis is related to host immune inflammatory factors and to a synergistic and dysbiotic oral microbiome (Hajishengallis et al. 2012; Hajishengallis 2014; Hajishengallis and Lamont 2014). In light of the diversity of the human oral microbiome (Proctor et al. 2018), immunoinformatics brings tools that provide faster analysis of virulence factors, considering the polymicrobial character of chronic periodontitis, and contribute to understanding the interaction between the oral microbiome and the host.

It is known that in silico models are used for understanding biological systems as well as to select, to complement, and to inspire the required laboratory experiments (Kollmann and Sourjik 2007; Setty 2014; Brodland 2015). In this context, immunoinformatics brings advances in immunology and can contribute to understanding the immune response (Lefranc 2014; Qiu et al. 2018). In the present study, the in silico analysis enabled the prediction and selection of immunoreactive peptides of P. gingivalis before being synthesized. Two virulence factors of P. gingivalis were analyzed: Kgp, which is widely studied, and neuraminidase, which is still being evaluated in a few studies.




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