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Computer-Aided Multi-Epitope Vaccine Design against Enterobacter xiangfangensis.

ABSTRACT: Antibiotic resistance is a global public health threat and is associated with high mortality due to antibiotics' inability to treat bacterial infections. Enterobacter xiangfangensis is an emerging antibiotic-resistant bacterial pathogen from the Enterobacter genus and has the ability to acquire resistance to multiple antibiotic classes. Currently, there is no effective vaccine against Enterobacter species. In this study, a chimeric vaccine is designed comprising different epitopes screened from E. xiangfangensis proteomes using immunoinformatic and bioinformatic approaches. In the first phase, six fully sequenced proteomes were investigated by bacterial pan-genome analysis, which revealed that the pathogen consists of 21,996 core proteins, 3785 non-redundant proteins and 18,211 redundant proteins. The non-redundant proteins were considered for the vaccine target prioritization phase where different vaccine filters were applied. By doing so, two proteins; ferrichrome porin (FhuA) and peptidoglycan-associated lipoprotein (Pal) were shortlisted for epitope prediction. Based on properties of antigenicity, allergenicity, water solubility and DRB*0101 binding ability, three epitopes (GPAPTIAAKR, ATKTDTPIEK and RNNGTTAEI) were used in multi-epitope vaccine designing. The designed vaccine construct was analyzed in a docking study with immune cell receptors, which predicted the vaccine's proper binding with said receptors. Molecular dynamics analysis revealed that the vaccine demonstrated stable binding dynamics, and binding free energy calculations further validated the docking results. In conclusion, these in silico results may help experimentalists in developing a vaccine against E. xiangfangensis in specific and Enterobacter in general.

SUBMITTER: Alshammari A

PROVIDER: S-EPMC9265868 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStreptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus.ResultsThe B- and T-cell epitopes from highly protective antigens PspA (clades 1-5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA-web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector.ConclusionThe computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.

Project description:Klebsiella is a genus of nosocomial bacterial pathogens and is placed in the most critical list of World Health Organization (WHO) for development of novel therapeutics. The pathogens of the genus are associated with high mortality and morbidity. Owing to their strong resistance profile against different classes of antibiotics and nonavailability of a licensed vaccine, urgent efforts are required to develop a novel vaccine candidate that can tackle all pathogenic species of the Klebsiella genus. The present study aims to design a broad-spectrum vaccine against all species of the Klebsiella genus with objectives to identify the core proteome of pathogen species, prioritize potential core vaccine proteins, analyze immunoinformatics of the vaccine proteins, construct a multi-epitopes vaccine, and provide its biophysical analysis. Herein, we investigated all reference species of the genus to reveal their core proteome. The core proteins were then subjected to multiple reverse vaccinology checks that are mandatory for the prioritization of potential vaccine candidates. Two proteins (TonB-dependent siderophore receptor and siderophore enterobactin receptor FepA) were found to fulfill all vaccine parameters. Both these proteins harbor several potent B-cell-derived T-cell epitopes that are antigenic, nonallergic, nontoxic, virulent, water soluble, IFN-γ producer, and efficient binder of DRB*0101 allele. The selected epitopes were modeled into a multi-epitope peptide comprising linkers and Cholera Toxin B adjuvant. For docking with innate immune and MHC receptors and afterward molecular dynamics simulations and binding free energy analysis, the vaccine structure was modeled for tertiary structure and refined for structural errors. To assess the binding affinity and presentation of the designed vaccine construct, binding mode and interactions analysis were performed using molecular docking and molecular dynamics simulation techniques. These biophysical approaches illustrated the vaccine as a good binder to the immune receptors and revealed robust interactions energies. The vaccine sequence was further translated to nucleotide sequence and cloned into an appropriate vector for expressing it at high rate in Escherichia coli K12 strain. In addition, the vaccine was illustrated to generate a good level of primary, secondary, and tertiary immune responses, proving good immunogenicity of the vaccine. Based on the reported results, the vaccine can be a good candidate to be evaluated for effectiveness in wet laboratory validation studies.

Project description:BackgroundInfluenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.ResultsThis study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector.ConclusionsThe proposed "Recombinant multi-epitope vaccine" was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions.

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Computer-Aided Multi-Epitope Vaccine Design against Enterobacter xiangfangensis.

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