Prediction of T-cell epitopes for designing a reverse vaccine against streptococcal bacteria.
Ontology highlight
ABSTRACT:
SUBMITTER: Ebrahimi S
PROVIDER: S-EPMC5991529 | biostudies-literature | 2018 Mar
REPOSITORIES: biostudies-literature
Publications
Prediction of T-cell epitopes for designing a reverse vaccine against streptococcal bacteria.
Ebrahimi Samira S Mohabatkar Hassan H
Molecular biology research communications 20180301 1
Streptococcal bacteria are among dangerous human pathogens with major prevalence worldwide. A good vaccine against streptococcal bacteria should have epitopes that confer protection from infection by different streptococcal bacteria types. we aimed was to recognize the most immunogenic and conserved epitopes of streptococcal bacteria, which could be a potential candidate for vaccine development. Nineteen different M proteins of different streptococcal bacteria were chosen and analyzed. Nine-mer ...[more]
Similar Datasets
Project description:A successful vaccine needs to target multiple strains of an organism. Streptococcus pyogenes is an organism that utilizes antigenic strain variation as a successful defence mechanism to circumvent the host immune response. Despite numerous efforts, there is currently no vaccine available for this organism. Here we review and discuss the significant obstacles to vaccine development, with a focus on how cryptic epitopes may provide a strategy to circumvent the obstacles of antigenic variation.
| S-EPMC6150013 | biostudies-literature
Project description:One of the most dangerous human pathogens with high prevalence worldwide is Streptococcus pyogenes, which has major impacts on global morbidity and mortality. A major challenge for S. pyogenes vaccine development is the detection of epitopes that confer protection from infection by multiple S. pyogenes types. Our aim was to identify the most conserved and immunogenic antigens of S. pyogenes, which can be a potential candidate for vaccine design in the future. Eight important surface proteins were analyzed. Using different prediction servers, strongest epitopes were selected. They had the ability to stimulate the humoral and cell-mediated immune system. Molecular docking was performed for measuring free-binding energy of selected epitopes. Seven epitopes from three surface proteins were selected as potential candidates for vaccine development. Conservation of selected epitopes among different Streptococcus types was checked. Further in vitro and in vivo tests are required to validate the suitability of the epitopes for vaccine design.
| S-EPMC7091373 | biostudies-literature
Project description:Mycobacteroides abscessus (Previously Mycobacterium abscessus) is an emerging microorganism of the newly defined genera Mycobacteroides that causes mainly skin and tissue diseases in humans. The recent availability of total 34 fully sequenced genomes of different strains belonging to this species has provided an opportunity to utilize this genomics data to gain novel insights and guide the development of specific antimicrobial therapies. In the present study, we collected collectively 34 complete genome sequences of M. abscessus from the NCBI GenBank database. Pangenome analysis was conducted on these genomes to understand the genetic diversity and to obtain proteins associated with its core genome. These core proteins were then subjected to various subtractive filters to identify potential antigenic targets that were subjected to multi-epitope vaccine design. Our analysis projected the open pangenome of M. abscessus containing 3443 core genes. After applying various stepwise filtration steps on the core proteins, a total of four potential antigenic targets were identified. Utilizing their constituent CD4 and CD8 T-cell epitopes, a multi-epitope based subunit vaccine was computationally designed. Sequence-based analysis as well as structural characterization revealed the immunological effectiveness of this designed vaccine. Further molecular docking, molecular dynamics simulation and binding free energy estimation with Toll-like receptor 2 indicated strong structural associations of the vaccine with the immune receptor. The promising results are encouraging and need to be validated by additional wet laboratory studies for confirmation.
| S-EPMC8159972 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19) linked with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause severe illness and life-threatening pneumonia in humans. The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection. Therefore, the present study was aimed to design a multiepitope-based subunit vaccine (MESV) against COVID-19.MethodsStructural proteins (Surface glycoprotein, Envelope protein, and Membrane glycoprotein) of SARS-CoV-2 are responsible for its prime functions. Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B- and T- cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV.ResultsPredicted epitopes suggested high antigenicity, conserveness, substantial interactions with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 88.40%. Taken together, 276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes (CTL), 6 helper T lymphocyte (HTL) and 4 B-cell epitopes with suitable adjuvant and linkers. The MESV construct was non-allergenic, stable, and highly antigenic. Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3 (TLR3). Furthermore, in silico immune simulation revealed significant immunogenic response of MESV. Finally, MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system, to ensure its increased expression.ConclusionThe MESV developed in this study is capable of generating immune response against COVID-19. Therefore, if designed MESV further investigated experimentally, it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19.
| S-EPMC7492789 | biostudies-literature
Project description:Ebola virus (EBOV) is a dangerous zoonotic infectious disease. To date, more than 25 EBOV outbreaks have been documented, the majority of which have occurred in Central Africa. The rVSVG-ZEBOV-GP vaccine (ERVEBO), a live attenuated vaccine, has been approved by the US Food and Drug Administration (FDA) to combat EBOV. Because of the several drawbacks of live attenuated vaccines, multi-epitope vaccines probably appear to be safer than live attenuated vaccines. In this work, we employed immunoinformatics tools to design a multi-epitope vaccine against EBOV. We collected sequences of VP35, VP24, VP30, VP40, GP, and NP proteins from the NCBI database. T-cell and linear B-cell epitopes from target proteins were identified and tested for antigenicity, toxicity, allergenicity, and conservancy. The selected epitopes were then linked together in the vaccine's primary structure using appropriate linkers, and the 50S ribosomal L7/L12 (Locus RL7 MYCTU) sequence was added as an adjuvant to the vaccine construct's N-terminal. The physicochemical, antigenicity, and allergenicity parameters of the vaccine were all found to be satisfactory. The 3D model of the vaccine was predicted, refined, and validated. The vaccine construct had a stable and strong interaction with toll-like receptor 4 (TLR4) based on molecular docking and molecular dynamic simulation (MD) analysis. The results of codon optimization and in silico cloning revealed that the proposed vaccine was highly expressed in Escherichia coli (E. coli). The findings of this study are promising; however, experimental validations should be carried out to confirm these findings.
| S-EPMC9094136 | biostudies-literature
Project description:Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd.edu.in/raghava/vactarbac/).
| S-EPMC6190870 | biostudies-literature
Project description:Since 2019, the world was involved with SARS-CoV-2 and consequently, with the announcement by the World Health Organization that COVID-19 was a pandemic, scientific were an effort to obtain the best approach to combat this global dilemma. The best way to prevent the pandemic from spreading further is to use a vaccine against COVID-19. Here, we report the design of a recombinant multi-epitope vaccine against the four proteins spike or crown (S), membrane (M), nucleocapsid (N), and envelope (E) of SARS-CoV-2 using immunoinformatics tools. We evaluated the most antigenic epitopes that bind to HLA class 1 subtypes, along with HLA class 2, as well as B cell epitopes. Beta-defensin 3 and PADRE sequence were used as adjuvants in the structure of the vaccine. KK, GPGPG, and AAY linkers were used to fuse the selected epitopes. The nucleotide sequence was cloned into pET26b(+) vector using restriction enzymes XhoI and NdeI, and HisTag sequence was considered in the C-terminal of the construct. The results showed that the proposed candidate vaccine is a 70.87 kDa protein with high antigenicity and immunogenicity as well as non-allergenic and non-toxic. A total of 95% of the selected epitopes have conservancy with similar sequences. Molecular docking showed a strong binding between the vaccine structure and tool-like receptor (TLR) 7/8. The docking, molecular dynamics, and MM/PBSA analysis showed that the vaccine established a stable interaction with both structures of TLR7 and TLR8. Simulation of immune stimulation by this vaccine showed that it evokes immune responses related to humoral and cellular immunity.
| S-EPMC8450176 | biostudies-literature
Project description:BackgroundBurkholderia pseudomallei is an infectious agent causing severe disease melioidosis resulting in pneumonia, fever, and acute septicemia in humans. B. pseudomallei show resistance to drugs. No such FDA-approved vaccine is available against B. pseudomallei, and treatment is limited to therapy. Therefore, the scientific study was designed to develop a vaccine for B. pseudomallei. The protein sequence of B. pseudomallei was retrieved from NCBI. B-cell and T-cell epitopes were identified and further screened for allergenicity, antigenicity docking, and simulation.ResultsHere, in this study, in silico approach was applied to design a multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of proteins considered to be potential novel vaccine candidates. Peptide epitopes were joined by adjuvant and EAAAK, CPGPG, and AAY linkers. This constructed vaccine was subjected to in silico immune simulations by C-ImmSim. The protein construct was cloned into PET28a (+) vector for expression study in Escherichia coli using SnapGene.ConclusionThe designed multi-epitope vaccine was analyzed for its physicochemical, structural, and immunological characteristics, and it was found to be antigenic, soluble, stable, nonallergenic, and have a high affinity to its target receptor. The immune simulation studies were carried out on the C-ImmSim showing increased production of cellular and humoral responses indicating that the constructed vaccine proved effective and able to provoke humoral and cell-mediated response immune responses. In silico study could be a breakthrough in designing effective vaccines to eradicate B. pseudomallei globally.
| S-EPMC9275536 | biostudies-literature
Project description:BackgroundMycobacterium ulcerans is the fundamental agent of the third most common Mycobacterial disease known as Buruli Ulcer (BU). It is an infection of the skin and soft tissue affecting the human population worldwide. Presently, the vaccine is not available against BU.ObjectiveThis study aimed to investigate the vaccine potential of virulence proteins of M. ulcerans computationally.MethodsChromosome encoded virulence proteins of Mycobacterium ulcerans strain Agy99 were selected, which were available at the VFDB database. These proteins were analyzed for their subcellular localization, antigenicity, and human non-homology analysis. Ten virulence factors were finally chosen and analyzed for further study. Three-dimensional structures for selected proteins were predicted using Phyre2. B cell and T cell epitope analysis was done using methods available at Immune Epitope Database and Analysis Resource. Antigenicity, allergenicity, and toxicity analysis were also done to predict epitopes. Molecular docking analysis was done for T cell epitopes, those showing overlap with B cell epitopes.ResultsSelected virulence proteins were predicted with B cell and T cell epitopes. Some of the selected proteins were found to be already reported as antigenic in other mycobacteria. Some of the predicted epitopes also had similarities with experimentally identified epitopes of M. ulcerans and M. tuberculosis which further supported our predictions.ConclusionIn-silico approach used for the vaccine candidate identification predicted some virulence proteins that could be proved important in future vaccination strategies against this chronic disease. Predicted epitopes require further experimental validation for their potential use as peptide vaccines.
| S-EPMC8905639 | biostudies-literature
Project description:High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV.This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV.This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV.
| S-EPMC4682406 | biostudies-other