Computational Design and Analysis of a Multi-epitope Against Influenza A virus.
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ABSTRACT:
SUBMITTER: Rostaminia S
PROVIDER: S-EPMC8435298 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC8808469 | biostudies-literature
Project description:Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine-TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.
| S-EPMC8894597 | biostudies-literature
Project description:Until now, design of the annual influenza vaccine has relied on phylogenetic or whole-sequence comparisons of the viral coat proteins hemagglutinin and neuraminidase, with vaccine effectiveness assumed to correlate monotonically to the vaccine-influenza sequence difference. We use a theory from statistical mechanics to quantify the non-monotonic immune response that results from antigenic drift in the epitopes of the hemagglutinin and neuraminidase proteins. The results explain the ineffectiveness of the 2003-2004 influenza vaccine in the United States and provide an accurate measure by which to optimize the effectiveness of future annual influenza vaccines.
| S-EPMC4482133 | biostudies-other
Project description:A novel infectious agent, SARS-CoV-2, is responsible for causing the severe respiratory disease COVID-19 and death in humans. Spike glycoprotein plays a key role in viral particles entering host cells, mediating receptor recognition and membrane fusion, and are considered useful targets for antiviral vaccine candidates. Therefore, computational techniques can be used to design a safe, antigenic, immunogenic, and stable vaccine against this pathogen. Drawing upon the structure of the S glycoprotein, we are trying to develop a potent multi-epitope subunit vaccine against SARS-CoV-2. The vaccine was designed based on cytotoxic T-lymphocyte and helper T-lymphocyte epitopes with an N-terminal adjuvant via conducting immune filters and an extensive immunoinformatic investigation. The safety and immunogenicity of the designed vaccine were further evaluated via using various physicochemical, allergenic, and antigenic characteristics. Vaccine-target (toll-like receptors: TLR2 and TLR4) interactions, binding affinities, and dynamical stabilities were inspected through molecular docking and molecular dynamic (MD) simulation methods. Moreover, MD simulations for dimeric TLRs/vaccine in the membrane-aqueous environment were performed to understand the differential domain organization of TLRs/vaccine. Further, dynamical behaviors of vaccine/TLR systems were inspected via identifying the key residues (named HUB nodes) that control interaction stability and provide a clear molecular mechanism. The obtained results from molecular docking and MD simulation revealed a strong and stable interaction between vaccine and TLRs. The vaccine's ability to stimulate the immune response was assessed by using computational immune simulation. This predicted a significant level of cytotoxic T cell and helper T cell activation, as well as IgG, interleukin 2, and interferon-gamma production. This study shows that the designed vaccine is structurally and dynamically stable and can trigger an effective immune response against viral infections.
| S-EPMC8981096 | biostudies-literature
Project description:ObjectiveInfluenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.MethodsThe present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.ResultsComputational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.ConclusionsWe propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.
| S-EPMC9095894 | biostudies-literature
Project description:Blackleg is an infectious disease of animals that is commonly caused by Clostridium chauvoei and characterized by localized muscle necrosis. In this study, proteome-mining and immunoinformatics approaches were applied to identify novel antigenic proteins and to construct a multi-epitope vaccine against C. chauvoei. All proteins of C. chauvoei strains were retrieved from the NCBI Microbial Genome Database containing both genomic and proteomic data of prokaryotes. The proteins were analyzed to exclude non-redundant sequences and to determine antigenic, virulent, and non-allergenic vaccine candidates through several online tools, resulting in seven protein candidates. Cytotoxic T and B cell epitopes of these proteins were evaluated through the tools present in the immune epitope database and the prioritized antigenic epitopes were then conjugated via appropriate linkers to construct the vaccine candidate. After the evaluation of physicochemical properties of the construct, the tertiary structure was modeled and refined through trRosetta and GalaxyRefine, respectively. The quality of the 3D structure was validated by ERRAT score, z-score, and Ramachandran plot and the construct was then docked with bovine Toll-like receptor 4 (TLR 4) using ClusPro. The docked complex was subjected to Molecular Mechanics/Generalized Born Surface Area in the HawkDock server and normal mode analysis in the iMODS simulation suite to assess the binding energy and stability of the complex, respectively. Overall, the vaccine construct was found stable and energetically feasible for bovine TLR 4 binding. Therefore, it can be used as a multi-epitope vaccine construct in clostridial vaccines to control the blackleg disease.Supplementary informationThe online version contains supplementary material available at 10.1007/s10989-021-10279-9.
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Project description:: Onchocerciasis is a skin and eye disease that exerts a heavy socio-economic burden, particularly in sub-Saharan Africa, a region which harbours greater than 96% of either infected or at-risk populations. The elimination plan for the disease is currently challenged by many factors including amongst others; the potential emergence of resistance to the main chemotherapeutic agent, ivermectin (IVM). Novel tools, including preventative and therapeutic vaccines, could provide additional impetus to the disease elimination tool portfolio. Several observations in both humans and animals have provided evidence for the development of both natural and artificial acquired immunity. In this study, immuno-informatics tools were applied to design a filarial-conserved multi-epitope subunit vaccine candidate, (designated Ov-DKR-2) consisting of B-and T-lymphocyte epitopes of eight immunogenic antigens previously assessed in pre-clinical studies. The high-percentage conservation of the selected proteins and epitopes predicted in related nematode parasitic species hints that the generated chimera may be instrumental for cross-protection. Bioinformatics analyses were employed for the prediction, refinement, and validation of the 3D structure of the Ov-DKR-2 chimera. In-silico immune simulation projected significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2 responses. Preliminary immunological analyses revealed that the multi-epitope vaccine candidate reacted with antibodies in sera from both onchocerciasis-infected individuals, endemic normals as well as loiasis-infected persons but not with the control sera from European individuals. These results support the premise for further characterisation of the engineered protein as a vaccine candidate for onchocerciasis.
| S-EPMC7912539 | biostudies-literature
Project description:Marburg virus (MARV) is a highly contagious and virulent agent belonging to Filoviridae family. MARV causes severe hemorrhagic fever in humans and non-human primates. Owing to its highly virulent nature, preventive approaches are promising for its control. There is currently no approved drug or vaccine against MARV, and management mainly involves supportive care to treat symptoms and prevent complications. Our aim was to design a novel multi-epitope vaccine (MEV) against MARV using immunoinformatics studies. In this study, various proteins (VP35, VP40 and glycoprotein precursor) were used and potential epitopes were selected. CTL and HTL epitopes covered 79.44% and 70.55% of the global population, respectively. The designed MEV construct was stable and expressed in Escherichia coli (E. coli) host. The physicochemical properties were also acceptable. MARV MEV candidate could predict comprehensive immune responses such as those of humoral and cellular in silico. Additionally, efficient interaction to toll-like receptor 3 (TLR3) and its agonist (β-defensin) was predicted. There is a need for validation of these results using further in vitro and in vivo studies.
| S-EPMC11227231 | biostudies-literature
Project description:Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.Supplementary informationThe online version contains supplementary material available at 10.1007/s13205-022-03286-0.
| S-EPMC9395775 | biostudies-literature
Project description:BackgroundDespite being in the 21st century, the world has still not been able to vanquish the global AIDS epidemic, and the only foreseeable solution seems to be a safe and effective vaccine. Unfortunately, vaccine trials so far have returned unfruitful results, possibly due to their inability to induce effective cellular, humoral and innate immune responses. The current study aims to tackle these limitations and propose the desired vaccine utilizing immunoinformatic approaches that have returned promising results in designing vaccines against various rapidly mutating organisms. For this, all polyprotein and protein sequences of HIV-1 were retrieved from the LANL (Los Alamos National Laboratory) database. The consensus sequence was generated after alignment and used to predict epitopes. Conserved, antigenic, non-allergenic, T-cell inducing, B-cell inducing, IFN-ɣ inducing, non-human homologous epitopes were selected and combined to propose two vaccine constructs i.e., HIV-1a (without adjuvant) and HIV-1b (with adjuvant).ResultsHIV-1a and HIV-1b were subjected to antigenicity, allergenicity, structural quality analysis, immune simulations, and MD (molecular dynamics) simulations. Both proposed multi-epitope vaccines were found to be antigenic, non-allergenic, stable, and induce cellular, humoral, and innate immune responses. TLR-3 docking and in-silico cloning of both constructs were also performed.ConclusionOur results indicate HIV-1b to be more promising than HIV-1a; experimental validations can confirm the efficacy and safety of both constructs and in-vivo efficacy in animal models.
| S-EPMC10206567 | biostudies-literature