Prediction of CTL epitope, in silico modeling and functional analysis of cytolethal distending toxin (CDT) protein of Campylobacter jejuni

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

Identification of potential candidate vaccines against Mycobacterium ulcerans based on the major facilitator superfamily transporter protein

Buruli ulcer is a neglected tropical disease that is characterized by non-fatal lesion development. The causative agent is Mycobacterium ulcerans (M. ulcerans). There are no known vectors or transmission methods, preventing the development of control methods. There are effective diagnostic techniques and treatment routines; however, several socioeconomic factors may limit patients' abilities to receive these treatments. The Bacillus Calmette-Guérin vaccine developed against tuberculosis has shown limited efficacy, and no conventionally designed vaccines have passed clinical trials. This study aimed to generate a multi-epitope vaccine against M. ulcerans from the major facilitator superfamily transporter protein using an immunoinformatics approach. Twelve M. ulcerans genome assemblies were analyzed, resulting in the identification of 11 CD8+ and 7 CD4+ T-cell epitopes and 2 B-cell epitopes. These conserved epitopes were computationally predicted to be antigenic, immunogenic, non-allergenic, and non-toxic. The CD4+ T-cell epitopes were capable of inducing interferon-gamma and interleukin-4. They successfully bound to their respective human leukocyte antigens alleles in in silico docking studies. The expected global population coverage of the T-cell epitopes and their restricted human leukocyte antigens alleles was 99.90%. The population coverage of endemic regions ranged from 99.99% (Papua New Guinea) to 21.81% (Liberia). Two vaccine constructs were generated using the Toll-like receptors 2 and 4 agonists, LprG and RpfE, respectively. Both constructs were antigenic, non-allergenic, non-toxic, thermostable, basic, and hydrophilic. The DNA sequences of the vaccine constructs underwent optimization and were successfully in-silico cloned with the pET-28a(+) plasmid. The vaccine constructs were successfully docked to their respective toll-like receptors. Molecular dynamics simulations were carried out to analyze the binding interactions within the complex. The generated binding energies indicate the stability of both complexes. The constructs generated in this study display severable favorable properties, with construct one displaying a greater range of favorable properties. However, further analysis and laboratory validation are required.

A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum

Avian mycoplasma is a bacterial disease causing chronic respiratory disease (CRD) in poultry industries with high economic losses. The eradication of this disease still remains as a challenge. A multi-epitope prophylactic vaccine aiming the antigenic proteins of Mycoplasma gallisepticum can be a capable candidate to eradicate this infection. The present study is focused to design a multi-epitope vaccine candidate consisting of cytotoxic T-cell (CTL), helper T-cell (HTL), and B-cell epitopes of antigenic proteins, using immunoinformatics strategies. The multi-epitopic vaccine was designed, and its tertiary model was predcited, which was further refined and validated by computational tools. After initial validation, molecular docking was performed between multi-epitope vaccine construct and chicken TLR-2 and 5 receptors, which predicted effective binding. The in silico results specify the structural stability, precise specificity, and immunogenic response of the designed multi-epitope vaccine, and it could be an appropriate vaccine candidate for the M. gallisepticum infection.

Multi-epitope-Based Vaccine Designed by Targeting Cytoadherence Proteins of Mycoplasma gallisepticum

Mycoplasma gallisepticum causes chronic respiratory disease in chickens leading to large economic losses in the poultry industry, and the impacts remain to be a great challenge for a longer period. Among the other approaches, a vaccine targeting the adhesion proteins of M. gallisepticum would be a promising candidate in controlling the infection. Thus, the present study is aimed to design a multi-epitope vaccine candidate using cytoadhesion proteins of M. gallisepticum through an advanced immunoinformatics approach. As a result, the multi-epitope vaccine was constructed, which comprised potential T-cell and B-cell binding epitopes with appropriate adjuvants. The designed multi-epitope vaccine represented high antigenicity with viable physiochemical properties. The prospective three-dimensional structure of the epitope was predicted, refined, and validated. The molecular docking analysis of multi-epitope vaccine candidates with the chicken Toll-like receptor-5 predicted effective binding. Furthermore, codon optimization and in silico cloning ensured high expression. Thus, the present finding indicates that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response. Furthermore, the multi-epitope vaccine is suggested to be a suitable vaccine candidate for the M. gallisepticum infection due to its effective binding capacity and precise specificity.

Immunoinformatics Approach for Multiepitopes Vaccine Prediction against Glycoprotein B of Avian Infectious Laryngotracheitis Virus

Infectious laryngotracheitis virus (ILTV) is a gallid herpesvirus type 1, a member of the genus Iltovirus. It causes an infection in the upper respiratory tract mainly trachea which results in significant economic losses in the poultry industry worldwide. Vaccination against ILTV produced latent infected carriers' birds, which become a source of virus transmission to nonvaccinated flocks. Thus this study aimed to design safe multiepitopes vaccine against glycoprotein B of ILT virus using immunoinformatic tools. Forty-four sequences of complete envelope glycoprotein B were retrieved from GenBank of National Center for Biotechnology Information (NCBI) and aligned for conservancy by multiple sequence alignment (MSA). Immune Epitope Database (IEDB) analysis resources were used to predict and analyze candidate epitopes that could act as a promising peptide vaccine. For B cell epitopes, thirty-one linear epitopes were predicted using Bepipred. However eight epitopes were found to be on both surface and antigenic epitopes using Emini surface accessibility and antigenicity, respectively. Three epitopes (₁₉₀KKLP₁₉₃, ₃₈₆YSSTHVRS₃₉₃, and ₃₁₇KESV₃₂₀) were proposed as B cell epitopes. For T cells several epitopes were interacted with MHC class I with high affinity and specificity, but the best recognized epitopes were ₁₁₈YVFNVTLYY₁₂₆, ₃₃₅VSYKNSYHF₃₄₃, and ₆₂₂YLLYEDYTF₆₃₀. MHC-II binding epitopes, ₃₀₁FLTDEQFTI₃₀₉,₂₇₇FLEIANYQV₂₈₅, and ₇₄₃IASFLSNPF₇₅₁, were proposed as promising epitopes due to their high affinity for MHC-II molecules. Moreover the docked ligand epitopes from MHC-1 molecule exhibited high binding affinity with the receptors; BF chicken alleles (BF2 2101 and 0401) expressed by the lower global energy of the molecules. In this study nine epitopes were predicted as promising vaccine candidate against ILTV. In vivo and in vitro studies are required to support the effectiveness of these predicted epitopes as a multipeptide vaccine through clinical trials.

Bridging immunogenetics and immunoproteomics: Model positional scanning library analysis for Major Histocompatibility Complex class II DQ in Tursiops truncatus

The Major Histocompatibility Complex (MHC) is a critical element in mounting an effective immune response in vertebrates against invading pathogens. Studies of MHC in wildlife populations have typically focused on assessing diversity within the peptide binding regions (PBR) of the MHC class II (MHC II) family, especially the DQ receptor genes. Such metrics of diversity, however, are of limited use to health risk assessment since functional analyses (where changes in the PBR are correlated to recognition/pathologies of known pathogen proteins), are difficult to conduct in wildlife species. Here we describe a means to predict the binding preferences of MHC proteins: We have developed a model positional scanning library analysis (MPSLA) by harnessing the power of mixture based combinatorial libraries to probe the peptide landscapes of distinct MHC II DQ proteins. The algorithm provided by NNAlign was employed to predict the binding affinities of sets of peptides generated for DQ proteins. These binding affinities were then used to retroactively construct a model Positional Scanning Library screen. To test the utility of the approach, a model screen was compared to physical combinatorial screens for human MHC II DP. Model library screens were generated for DQ proteins derived from sequence data from bottlenose dolphins from the Indian River Lagoon (IRL) and the Atlantic coast of Florida, and compared to screens of DQ proteins from Genbank for dolphin and three other cetaceans. To explore the peptide binding landscape for DQ proteins from the IRL, combinations of the amino acids identified as active were compiled into peptide sequence lists that were used to mine databases for representation in known proteins. The frequency of which peptide sequences predicted to bind the MHC protein are found in proteins from pathogens associated with marine mammals was found to be significant (p values <0.0001). Through this analysis, genetic variation in MHC (classes I and II) can now be associated with the binding repertoires of the expressed MHC proteins and subsequently used to identify target pathogens. This approach may be eventually applied to evaluate individual population and species risk for outbreaks of emerging diseases.

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

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.

Prediction and In Silico Identification of Novel B-Cells and T-Cells Epitopes in the S1-Spike Glycoprotein of M41 and CR88 (793/B) Infectious Bronchitis Virus Serotypes for Application in Peptide Vaccines

Bioinformatic analysis was used to predict antigenic B-cell and T-cell epitopes within the S1 glycoprotein of M41 and CR88 IBV strains. A conserved linear B-cell epitope peptide, YTSNETTDVTS^175–185, was identified in M41 IBV strains while three such epitopes types namely, VSNASPNSGGVD^279–290, HPKCNFRPENI^328–338, and NETNNAGSVSDCTAGT^54–69, were predicted in CR88 IBV strains. Analysis of MHCI binding peptides in M41 IBV strains revealed the presence of 15 antigenic peptides out of which 12 were highly conserved in 96–100% of the total M41 strains analysed. Interestingly three of these peptides, GGPITYKVM²⁰⁸, WFNSLSVSI³⁵⁶, and YLADAGLAI⁴⁷², relatively had high antigenicity index (>1.0). On the other hand, 11 MHCI binding epitope peptides were identified in CR88 IBV strains. Of these, five peptides were found to be highly conserved with a range between 90% and 97%. However, WFNSLSVSL³⁵⁸, SYNISAASV⁸⁸, and YNISAASVA⁸⁹ peptides comparably showed high antigenicity scores (>1.0). Combination of antigenic B-cells and T-cells peptides that are conserved across many strains as approach to evoke humoral and CTL immune response will potentially lead to a broad-based vaccine that could reduce the challenges in using live attenuated vaccine technology in the control of IBV infection in poultry.

In silico proposition to predict cluster of B- and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane associated proteins of C. jejuni

Purpose

Campylobacter jejuni is the one of the leading causes of bacterial diarrheal illness worldwide. This study aims to design specific epitopes for the utility of designing peptide vaccine(s) against C. jejuni by targeting invasive, virulent and membrane associated proteins like FlaA, Cia, CadF, PEB1, PEB3 and MOMP.

Methods

In the present study, various immunoinformatics approaches have been applied to design a potential epitope based vaccine against C. jejuni. The tools include Bepipred, ABCpred, Immune Epitope databse (IEDB) resource portal, Autodock vina etc.

Results

Peptides “EINKN”, “TGSRLN”, “KSNPDI”, “LDENGCE” respectively from FlaA, MOMP, PEB3, CadF proteins were found to be the most potential B cell epitopes while peptides “FRINTNVAA”, “NYFEGNLDM”, “YKYSPKLNF”, “YQDAIGLLV”, “FRNNIVAFV” and “LIMPVFHEL” respectively from Fla, CadF, MOMP, PEB1A, PEB3 and Cia might elicit cell mediated immunity and “IFYTTGSRL” from MOMP protein might elicit both humoral and cell-mediated immunity. All these potential peptidic epitopes showed almost 80–100 % conservancy in different strains of C jejuni with varying proportions of population coverage ranging from 22–60 %. Further authentication of these peptide epitopes as probable vaccine candidate was mediated by their binding to specific HLA alleles using in silico docking technique.

Conclusion

Based on the present study, it could be concluded that these predicted epitopes might be used to design a vaccine against C. jejuni bacteria and thus, could be validated in model hosts to verify their efficacy as vaccine.

Electronic supplementary material

The online version of this article (doi:10.1186/s40203-016-0020-y) contains supplementary material, which is available to authorized users.