Immunoinformatics Approach in Designing Epitope-based Vaccine against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b)

Abstract

Meningitis infection is one of the major threats during Hajj season in Mecca. Meningitis vaccines are available, but their uses are limited in some countries due to religious reasons. Furthermore, they only give protection to certain serogroups, not to all types of meningitis-inducing bacteria. Recently, research on epitope-based vaccines has been developed intensively. Such vaccines have potential advantages over conventional vaccines in that they are safer to use and well responded to the antibody. In this study, we developed epitope-based vaccine candidates against various meningitis-inducing bacteria, including Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae type b. The epitopes were selected from their protein of polysaccharide capsule. B-cell epitopes were predicted by using BCPred, while T-cell epitope for major histocompatibility complex (MHC) class I was predicted using PAProC, TAPPred, and Immune Epitope Database. Immune Epitope Database was also used to predict T-cell epitope for MHC class II. Population coverage and molecular docking simulation were predicted against previously generated epitope vaccine candidates. The best candidates for MHC class I- and class II-restricted T-cell epitopes were MQYGDKTTF, MKEQNTLEI, ECTEGEPDY, DLSIVVPIY, YPMAMMWRNASNRAI, TLQMTLLGIVPNLNK, ETSLHHIPGISNYFI, and SLLYILEKNAEMEFD, which showed 80% population coverage. The complexes of class I T-cell epitopes–HLA-C*03:03 and class II T-cell epitopes–HLA-DRB1*11:01 showed better affinity than standards as evaluated from their ΔGbinding value and the binding interaction between epitopes and HLA molecules. These peptide constructs may further be undergone in vitro and in vivo testings for the development of targeted vaccine against meningitis infection.

Immunobioinformatics analysis and phylogenetic tree construction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Indonesia: spike glycoprotein gene

“…The performance of this server was developed based on the physicochemical properties of the target protein without alignment. 19 In this study, we obtained 12 predicted peptides and 8 other peptides considered as protective antigens (Table 2 and Figure 1). B-cell epitope prediction is a methodology to predict protein regions that could be recognized as an epitope response to B-cells.…”

In silico vaccine design against Chlamydia trachomatis infection

“…Studies confirm the release of CD4+ T cells and IFN γ cells to protect from the infections. Thus, suggesting the importance of identification of T-cell epitopes epitope-based vaccine approach has been reported for Francisella tularensis (Whelan et al 2018), Haemophilus influenzae (Zahroh et al 2016), Dengue virus (Chakraborty et al 2010), Human coronaviruses (Oany et al 2014), Chikungunya virus (Islam et al 2012), and Ebola (Srivastava et al 2016).…”

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

“…After proteolytic cleavage of viral antigens, antigen presenting cells such as B cells, macrophages and dendritic cells present epitopes to HTLs or CD4+ T cells in the epitope-MHC class II complex form [98]. Almost all immunoinformatics studies for vaccine construction incorporate the prediction of HTL epitopes as part of their work [99][100][101]. In our study, 15-mer HTL epitopes and their corresponding MHC class II alleles were predicted using the consensus algorithm of the IEDB MHC-II binding tool [102].…”