Linear B cell epitope prediction for epitope vaccine design against meningococcal disease and their computational validations through physicochemical properties

Abstract

Neisseria meningitidis serogroup B is predominantly known for its leading role in bacterial meningitis and septicemia worldwide. Although, polysaccharide conjugate vaccines have been developed and used successfully against many of the serogroups of N. meningitidis, such strategy has proved ineffective against group B meningococci. Here, we proposed to develop peptide epitope-based vaccine candidates from outer membrane (OM) protein contained in the outer membrane vesicles (OMV) based on our in silico analysis. In OMV, a total of 236 proteins were identified, only 15 (6.4 %) of which were predicted to be located in the outer membrane. For the preparation of specific monoclonal antibodies against pathogenic bacterial protein, identification and selection of B cell epitopes that act as a vaccine target are required. We selected 13 outer membrane proteins from OMV proteins while taking into consideration the removal of cross-reactivity. Epitopia web server was used for the prediction of B cell epitopes. Epitopes are distinguished from non-epitopes by properties such as amino acid preference on the basis of amino acid composition, secondary structure composition, and evolutionary conservation. Predicted results were subject to verification with experimental data and we performed string-based search through IEDB. Our finding shows that epitopes have general preference for charged and polar amino acids; epitopes are enriched with loop as a secondary structure element that renders them flexible and also exposes another view of antibody–antigen interaction.