Surface proteome mining for identification of potential vaccine candidates against Campylobacter jejuni: an in silico approach

Abstract

Campylobacter jejuni remains a major cause of human gastroenteritis with estimated annual incidence rate of 450 million infections worldwide. C. jejuni is a major burden to public health in both socioeconomically developing and industrialized nations. Virulence determinants involved in C. jejuni pathogenesis are multifactorial in nature and not yet fully understood. Despite the completion of the first C. jejuni genome project in 2000, there are currently no vaccines in the market against this pathogen. Traditional vaccinology approach is an arduous and time extensive task. Omics techniques coupled with sequencing data have engaged researcher's attention to reduce the time and resources applied in the process of vaccine development. Recently, there has been remarkable increase in development of in silico analysis tools for efficiently mining biological information obscured in the genome. In silico approaches have been crucial for combating infectious diseases by accelerating the pace of vaccine development. This study employed a range of bioinformatics approaches for proteome scale identification of peptide vaccine candidates. Whole proteome of C. jejuni was investigated for varied properties like antigenicity, allergenicity, major histocompatibility class (MHC)-peptide interaction, immune cell processivity, HLA distribution, conservancy, and population coverage. Predicted epitopes were further tested for binding in MHC groove using computational docking studies. The predicted epitopes were conserved; covered more than 80 % of the world population and were presented by MHC-I supertypes. We conclude by underscoring that the epitopes predicted are believed to expedite the development of successful vaccines to control or prevent C. jejuni infections albeit the results need to be experimentally validated.

Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum

“…The VaxiJen2.0 program was used for the evaluation of antigenicity of the malarial adhesins protein sequence [ 27 , 28 ]. Furthermore, to assess the potential impact of membrane proximity, transmembrane regions of potential antigens (pathogenesis-related secretome of P. falciparum ) were predicted using TMHMM2.0 tool [ 29 – 31 ]. The physico-chemical characterization [(grand average of hydropathicity (Gravy), theoretical isoelectric point (pI)] and water solubility analysis of all the 5 GDPAA and 5 RSKMA were studied by using tools ExPASy-ProtParam and INNOVAGEN, respectively [ 32 – 34 ].…”

Developments in Rapid Detection Methods for the Detection of Foodborne Campylobacter in the United States

“…The application of proteomics to immune responses may prove to be especially helpful to find novel antibodies. Mehla and Ramana (2017) employed computational analyses based on informatics databases of the Campylobacter genome to identify and predict immunogenic epitopes that could stimulate B- and T-cell activity and serve as antigens for vaccine construction. It is not difficult to imagine that similar data mining approaches could be used to identify antigens for specific monoclonal antibody generation and EIA development that would enable optimal sensitivity and specificity.…”

In silico assessment of plant L-asparaginase and estimating its allergenicity in comparison to bacteria asparaginase

“…Additionally, some enzymes have been identified as an allergen in fish, midges, crustaceans, and various plants based on in silico Prediction of T and B Cell Epitopes [39][40][41][42]. Research has been performed on predicting protective continuous B-cell epitopes on plant pathogen proteins using the ABCpred server [43].…”