In Silico Identification of the B-Cell and T-Cell Epitopes of the Antigenic Proteins of Staphylococcus aureus for Potential Vaccines

2021 ◽  
pp. 439-447
Author(s):  
Sunil Thomas ◽  
Irini Doytchinova
Keyword(s):  
Staphylococcus Aureus ◽  
T Cell ◽  
B Cell ◽  
In Silico ◽  
T Cell Epitopes ◽  
Antigenic Proteins ◽  
In Silico Identification

In silico identification of immunodominant B-cell and T-cell epitopes of non-structural proteins of Usutu Virus

2018 ◽  
Vol 125 ◽  
pp. 129-143 ◽  
Author(s):  
Rohit Satyam ◽  
Essam Mohammed Janahi ◽  
Tulika Bhardwaj ◽  
Pallavi Somvanshi ◽  
Shafiul Haque ◽  
...  
Keyword(s):  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Structural Proteins ◽  
T Cell Epitopes ◽  
Usutu Virus ◽  
In Silico Identification

scholarly journals An immunoinformatics approach for the design of a multi-epitope vaccine targeting super antigen TSST-1 of Staphylococcus aureus

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Harish Babu Kolla ◽  
Chakradhar Tirumalasetty ◽  
Krupanidhi Sreerama ◽  
Vijaya Sai Ayyagari
Keyword(s):  
Staphylococcus Aureus ◽  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Three Dimensional ◽  
Clinical Manifestations ◽  
Food Poisoning ◽  
Dimensional Structure ◽  
T Cell Epitopes ◽  
Epitope Vaccine

Abstract Background TSST-1 is a secretory and pyrogenic superantigen that is being responsible for staphylococcal mediated food poisoning and associated clinical manifestations. It is one of the main targets for the construction of vaccine candidates against Staphylococcus aureus. Most of the vaccines have met failure due to adverse reactions and toxicity reported during late clinical studies. To overcome this, an immunoinformatics approach is being used in the present study for the design of a multi-epitope vaccine to circumvent the problems related to toxicity and allergenicity. Results In this study, a multi-epitope vaccine against Staphylococcus aureus targeting TSST-1 was designed through an immunoinformatics approach. B cell and T cell epitopes were predicted in silico and mapped with linkers to avoid junctional immunogenicity and to ensure the efficient presentation of exposed epitopes through HLA. β-defensin and PADRE were adjusted at the N-terminal end of the final vaccine as adjuvants. Physiochemical parameters, antigenicity, and allergenicity of the vaccine construct were determined with the help of online servers. The three-dimensional structure of the vaccine protein was predicted and validated with various tools. The affinity of the vaccine with TLR-3 was studied through molecular docking studies and the interactions of two proteins were visualized using LigPlot+. The vaccine was successfully cloned in silico into pET-28a (+) for efficient expression in E. coli K12 system. Population coverage analysis had shown that the vaccine construct can cover 83.15% of the global population. Immune simulation studies showed an increase in the antibody levels, IL-2, IFN-γ, TGF-β, B cell, and T cell populations and induced primary, secondary, and tertiary immune responses. Conclusion Multi-epitope vaccine designed through a computational approach is a non-allergic and non-toxic antigen. Preliminary in silico reports have shown that this vaccine could elicit both B cell and T cell responses in the host as desired.

scholarly journals Molecular cloning, expression, purification and in silico epitope prediction of cobalamin-independent methionine synthase (Mor a 2), as a novel allergen from Morus alba pollen

2021 ◽  
Author(s):  
Yunus AKSÜT
Keyword(s):  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Allergic Diseases ◽  
Structural Features ◽  
Morus Alba ◽  
Methionine Synthase ◽  
Antigenic Peptides ◽  
T Cell Epitopes ◽  
B Cell Epitopes

IntroductionMorus alba (white mulberry) pollen is an aero-allergen source that can trigger allergic diseases. Cobalamin-independent methionine synthase (MetE) in M. alba pollen has been proved to be one of the major allergens for some patients living in Istanbul (Turkey). The aim of the present study was the recombinant production and identification of MetE (Mor a 2), a novel allergen from M. alba pollen. The IgE binding reactivity of rMor a 2 produced for the first time was evaluated and some structural features were investigated by in silico methods to better understand its immunogenicity.Material and methodsThe gene encoding Mor a 2 was cloned in fission yeast, Schizosaccharomyces pombe ura4-D18h- strain, using pSLF1073 vector. This is the first report of the production of recombinant pollen allergen in S. pombe. After the purification, immunoreactivity of rMor a 2 was confirmed by immunoblotting using sera of patient allergic to M. alba pollen. Besides, B-cell epitopes of rMor a 2 were predicted using various bioinformatic tools, namely Bioinformatics Predicted Antigenic Peptides, BepiPred 2.0 and Immune Epitope Database whereas T-cell epitopes were estimated using NetMHCIIpan-3.2 and NetMHCII 2.3 servers.ResultsThe immunoblotting analysis yielded 11 of 11 positive reactions to rMor a 2. In silico predictions exerted seven B-cell epitopes (22-33, 384-394, 407-423, 547-553, 571-577, 671-678, 736-741) and seven T-cell epitopes (54-62, 161-170, 197-205, 347-358, 622-630, 657-665, 756-764).ConclusionsThese findings may help the use of rMor a 2 in the diagnosis and treatment of allergic diseases associated with M. alba and/or MetE.

scholarly journals Attenuated Subcomponent Vaccine Design Targeting the SARS-CoV-2 Nucleocapsid Phosphoprotein RNA Binding Domain: In Silico Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Onyeka S. Chukwudozie ◽  
Rebecca C. Chukwuanukwu ◽  
Onyekachi O. Iroanya ◽  
Daniel M. Eze ◽  
Vincent C. Duru ◽  
...  
Keyword(s):  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Rna Binding ◽  
Peptide Vaccine ◽  
In Silico Analysis ◽  
Dynamics Simulation ◽  
Effective Vaccine ◽  
Translation Efficiency ◽  
T Cell Epitopes

The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has previously never been identified with humans, thereby creating devastation in public health. The need for an effective vaccine to curb this pandemic cannot be overemphasized. In view of this, we designed a subcomponent antigenic peptide vaccine targeting the N-terminal (NT) and C-terminal (CT) RNA binding domains of the nucleocapsid protein that aid in viral replication. Promising antigenic B cell and T cell epitopes were predicted using computational pipelines. The peptides “RIRGGDGKMKDL” and “AFGRRGPEQTQGNFG” were the B cell linear epitopes with good antigenic index and nonallergenic property. Two CD8+ and Three CD4+ T cell epitopes were also selected considering their safe immunogenic profiling such as allergenicity, antigen level conservancy, antigenicity, peptide toxicity, and putative restrictions to a number of MHC-I and MHC-II alleles. With these selected epitopes, a nonallergenic chimeric peptide vaccine incapable of inducing a type II hypersensitivity reaction was constructed. The molecular interaction between the Toll-like receptor-5 (TLR5) which was triggered by the vaccine was analyzed by molecular docking and scrutinized using dynamics simulation. Finally, in silico cloning was performed to ensure the expression and translation efficiency of the vaccine, utilizing the pET-28a vector. This research, therefore, provides a guide for experimental investigation and validation.

scholarly journals An Integrated In-Silico Approach to Develop Epitope-Based Peptide Vaccine against SARS-CoV-2

2020 ◽  
Author(s):  
Prekshi Garg ◽  
Neha Srivastava ◽  
Prachi Srivastava
Keyword(s):  
T Cell ◽  
B Cell ◽  
In Silico ◽  
3D Structure ◽  
Peptide Vaccine ◽  
Cell Epitope ◽  
Peptide Sequence ◽  
T Cell Epitopes ◽  
Mhc I ◽  
In Silico Study

SARS-CoV-2 has been the talk of the town ever since the beginning of 2020. The pandemic has brought the complete world on a halt. Every country is trying all possible steps to combat the disease ranging from shutting the complete economy of the country to repurposing of drugs and vaccine development. The rapid data analysis and widespread tools, software and databases have made bioinformatics capable of giving new insights to the researchers to deal with the current scenario more efficiently. Vaccinomics, the new emerging field of bioinformatics uses concepts of immunogenetics and immunogenomics with in silico tools to give promising results for wet lab experiments. This approach is highly validated for the designing and development of potent vaccines. The present in-silico study was attempted to identify peptide fragments from spike surface glycoprotein that can be efficiently used for the designing and development of epitope-based vaccine designing approach. Both B-cell and T-cell epitopes are predicted using integrated computational tools. VaxiJen server was used for prediction of protective antigenicity of the protein. NetCTL was studied for analyzing most potent T cell epitopes and its subsequent MHC-I interaction through tools provided by IEDB. 3D structure prediction of peptides and MHC-I alleles (HLA-C*03:03) was further done to carry out docking studies using AutoDock4.0. Various tools from IEDB were used to predict B-cell epitopes on the basis of different essential parameters like surface accessibility, beta turns and many more. Based on results interpretation, the peptide sequence from 1138-1145 amino acid and sequence WTAGAAAYY and YDPLQPEL were obtained as a potential B-cell epitope and T-cell epitope respectively. This in-silico study will help us to identify novel epitope-based peptide vaccine target in spike protein of SARS-CoV-2. Further, in-vitro and in-vivo study needed to validate the findings.

scholarly journals In silico identification and modification of T cell epitopes in pertussis antigens associated with tolerance

2020 ◽  
Vol 16 (2) ◽  
pp. 277-285 ◽  
Author(s):  
Corine Kruiswijk ◽  
Guilhem Richard ◽  
Merijn L.M. Salverda ◽  
Pooja Hindocha ◽  
William D. Martin ◽  
...  
Keyword(s):  
T Cell ◽  
In Silico ◽  
T Cell Epitopes ◽  
In Silico Identification ◽  
Pertussis Antigens

In Silico Prediction of Epitopes in Virulence Proteins of Mycobacterium ulcerans for Vaccine Designing

2021 ◽  
Vol 22 ◽  
Author(s):  
Taruna Mohinani ◽  
Aditya Saxena ◽  
Shoor Vir Singh
Keyword(s):  
T Cell ◽  
B Cell ◽  
In Silico ◽  
Three Dimensional ◽  
Cell Epitope ◽  
Buruli Ulcer ◽  
Mycobacterium Ulcerans ◽  
T Cell Epitopes ◽  
Virulence Proteins ◽  
Vaccine Potential

Background: Mycobacterium 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. Objective: This study aimed to investigate the vaccine potential of virulence proteins of M. ulcerans computationally. Methods: Chromosome 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. Results: Selected 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. Conclusion : In-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.

Sign in / Sign up

Export Citation Format

Share Document