scholarly journals EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Magdalena Molero-Abraham ◽  
John-Paul Glutting ◽  
Darren R. Flower ◽  
Esther M. Lafuente ◽  
Pedro A. Reche
Keyword(s):  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Variola Virus ◽  
T Cell Epitopes ◽  
Membrane Structures ◽  
Class Ii Mhc ◽  
Vaccinia Viruses ◽  
Virus Genomes

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes.

scholarly journals Identification of a Promiscuous T-Cell Epitope in Mycobacterium tuberculosis Mce Proteins

2002 ◽  
Vol 70 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Maddalena Panigada ◽  
Tiziana Sturniolo ◽  
Giorgio Besozzi ◽  
Maria Giovanna Boccieri ◽  
Francesco Sinigaglia ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Cell Responses ◽  
Hla Dr

ABSTRACT The characterization of Mycobacterium tuberculosis antigens inducing CD4+ T-cell responses could critically contribute to the development of subunit vaccines for M. tuberculosis. Here we performed computational analysis by using T-cell epitope prediction software (known as TEPITOPE) to predict promiscuous HLA-DR ligands in the products of the mce genes of M. tuberculosis. The analysis of the proliferative responses of CD4+ T cells from patients with pulmonary tuberculosis to selected peptides displaying promiscuous binding to HLA-DR in vitro led us to the identification of a peptide that induced proliferation of CD4+ cells from 50% of the tested subjects. This study demonstrates that a systematic computational approach can be used to identify T-cell epitopes in proteins expressed by an intracellular pathogen.

scholarly journals Characterization of Plasmodium falciparum Proteome at Asexual Blood Stages for Screening of Effective Vaccine Candidates: An Immunoinformatics Approach

2015 ◽  
Vol 7 ◽  
pp. III.S24755 ◽  
Author(s):  
Satarudra Prakash Singh ◽  
Vishal Verma ◽  
Bhartendu Nath Mishra
Keyword(s):  
Plasmodium Falciparum ◽  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Proteome Analysis ◽  
Effective Vaccine ◽  
Secretory Proteins ◽  
Vaccine Candidates ◽  
Proteome Level

Malaria is a complex parasitic disease that is currently causing great concerns globally owing to the resistance to antimalarial drugs and lack of an effective vaccine. The present study involves the characterization of extracellular secretory proteins as vaccine candidates derived from proteome analysis of Plasmodium falciparum at asexual blood stages of malaria. Among the screened 32 proteins, 31 were predicted as antigens by the VaxiJen program, and 26 proteins had less than two transmembrane spanning regions predicted using the THMMM program. Moreover, 10 and 5 proteins were predicted to contain secretory signals by SignalP and TargetP, respectively. T-cell epitope prediction using MULTIPRED2 and NetCTL programs revealed that most of the predicted antigens are immunogenic and contain more than 10% supertype and 5% promiscuous epitopes of HLA-A, -B, or -DR. We anticipate that T-cell immune responses against asexual blood stages of Plasmodium are dispersed on a relatively large number of parasite antigens. This is the first report, to the best of our knowledge, offering new insights, at the proteome level, for the putative screening of effective vaccine candidates against the malaria pathogen. The findings also suggest new ways forward for the modern omics-guided vaccine target discovery using reverse vaccinology.

scholarly journals Immunoinformatics Approach Identified Two Highly Conserved B and T Cell Epitopes, LEASKRWAF and DSPLEASKRWAFRTG, for Effective Vaccine Design against Ebola and Marburg Viruses

2019 ◽  
pp. 1-16
Author(s):  
Md. Shahadat Hossain ◽  
Hasan Al. Reza ◽  
Mohammad Shahnoor Hossain
Keyword(s):  
T Cell ◽  
Mhc Class Ii ◽  
Simulation Analysis ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Docking Simulation ◽  
Hemorrhagic Fever ◽  
Effective Vaccine ◽  
T Cell Epitopes ◽  
Mhc Molecules

Aims: Ebola and Marburg viruses cause fatal hemorrhagic fever in both human and non-human primates. Absence of any licensed vaccine has further deteriorated the problem. In the present study, we aimed to design potential epitope based vaccines against these viruses using computational approaches. Methodology: By using various bioinformatics tools and databases, we analyzed the conserved glycoprotein sequences of Ebola and Marburg viruses and predicted two potential epitopes which may be used as peptide vaccines. Results: Using various B-cell and T-cell epitope prediction servers, four highly conserved epitopes were identified. Epitope conservancy analysis showed that  “LEASKRWAF” and “DSPLEASKRWAFRTG” epitopes were 100% and 93.62% conserved and the worldwide population coverage of “LEASKRWAF” interacting with MHC class I molecules and “DSPLEASKRWAFRTG” interacting with MHC class II molecules were 78.74% and 75.75% respectively. Immunoinformatics analysis showed that they are highly immunogenic, flexible and accessible to antibody. Molecular docking simulation analysis demonstrated a very significant interaction between epitopes and MHC molecules with lower binding energy. Cytotoxic analysis and ADMET test also supported their potential as vaccine candidates. Conclusion: In sum, our in silico approach demonstrated that both “LEASKRWAF” and “DSPLEASKRWAFRTG” hold the promise for the development of common vaccine against Ebola and Marburg viruses.

scholarly journals Prediction of T and B Cell Epitopes in the Proteome of SARS-CoV-2 for Potential Use in Diagnostics and Vaccine Design

2020 ◽  
Author(s):  
Parvez Slathia ◽  
Preeti Sharma,
Keyword(s):  
T Cell ◽  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Vaccine Design ◽  
T Cell Epitopes ◽  
B Cell Epitopes ◽  
The People ◽  
Skilled Manpower ◽  
Prophylactic Measures

<p>The world is currently battling the Covid-19 pandemic for which there is no therapy available. Prophylactic measures like vaccines can effectively thwart the disease burden. The current methods of detection are PCR based and require skilled manpower to operate. The availability of cheap and ready to use diagnostics like serological methods can ease the detection of SARS-CoV-2 virus. In the current study, immunoinformatics tools have been used to predict T and B cell epitopes present in all the proteins of this virus. NetMHCPan, NetCTL and NetMHCII servers were used for T cell epitope prediction while BepiPred and ABCPred were used for B cell epitope prediction. Population coverage analysis for T cell epitopes revealed that these could provide protection to the people throughout world. The T cell epitopes can exclusively used for vaccine design whereas B cell epitopes can be used for both vaccine design and developing diagnostic kits. </p> <p> </p>

scholarly journals Prediction of T and B Cell Epitopes in the Proteome of SARS-CoV-2 for Potential Use in Diagnostics and Vaccine Design

2020 ◽  
Author(s):  
Parvez Slathia ◽  
Preeti Sharma,
Keyword(s):  
T Cell ◽  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Vaccine Design ◽  
T Cell Epitopes ◽  
B Cell Epitopes ◽  
The People ◽  
Skilled Manpower ◽  
Prophylactic Measures

<p>The world is currently battling the Covid-19 pandemic for which there is no therapy available. Prophylactic measures like vaccines can effectively thwart the disease burden. The current methods of detection are PCR based and require skilled manpower to operate. The availability of cheap and ready to use diagnostics like serological methods can ease the detection of SARS-CoV-2 virus. In the current study, immunoinformatics tools have been used to predict T and B cell epitopes present in all the proteins of this virus. NetMHCPan, NetCTL and NetMHCII servers were used for T cell epitope prediction while BepiPred and ABCPred were used for B cell epitope prediction. Population coverage analysis for T cell epitopes revealed that these could provide protection to the people throughout world. The T cell epitopes can exclusively used for vaccine design whereas B cell epitopes can be used for both vaccine design and developing diagnostic kits. </p> <p> </p>

scholarly journals Immune Tolerance-Adjusted Personalized Immunogenicity Prediction for Pompe Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Anne S. De Groot ◽  
Ankit K. Desai ◽  
Sandra Lelias ◽  
S. M. Shahjahan Miah ◽  
Frances E. Terry ◽  
...  
Keyword(s):  
T Cell ◽  
Clinical Outcomes ◽  
Pompe Disease ◽  
Clinical Decision Making ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
T Cell Epitopes ◽  
Enzyme Replacement ◽  
Hla Dr

Infantile-onset Pompe disease (IOPD) is a glycogen storage disease caused by a deficiency of acid alpha-glucosidase (GAA). Treatment with recombinant human GAA (rhGAA, alglucosidase alfa) enzyme replacement therapy (ERT) significantly improves clinical outcomes; however, many IOPD children treated with rhGAA develop anti-drug antibodies (ADA) that render the therapy ineffective. Antibodies to rhGAA are driven by T cell responses to sequences in rhGAA that differ from the individuals’ native GAA (nGAA). The goal of this study was to develop a tool for personalized immunogenicity risk assessment (PIMA) that quantifies T cell epitopes that differ between nGAA and rhGAA using information about an individual’s native GAA gene and their HLA DR haplotype, and to use this information to predict the risk of developing ADA. Four versions of PIMA have been developed. They use EpiMatrix, a computational tool for T cell epitope identification, combined with an HLA-restricted epitope-specific scoring feature (iTEM), to assess ADA risk. One version of PIMA also integrates JanusMatrix, a Treg epitope prediction tool to identify putative immunomodulatory (regulatory) T cell epitopes in self-proteins. Using the JanusMatrix-adjusted version of PIMA in a logistic regression model with data from 48 cross-reactive immunological material (CRIM)-positive IOPD subjects, those with scores greater than 10 were 4-fold more likely to develop ADA (p&lt;0.03) than those that had scores less than 10. We also confirmed the hypothesis that some GAA epitopes are immunomodulatory. Twenty-one epitopes were tested, of which four were determined to have an immunomodulatory effect on T effector response in vitro. The implementation of PIMA V3J on a secure-access website would allow clinicians to input the individual HLA DR haplotype of their IOPD patient and the GAA pathogenic variants associated with each GAA allele to calculate the patient’s relative risk of developing ADA, enhancing clinical decision-making prior to initiating treatment with ERT. A better understanding of immunogenicity risk will allow the implementation of targeted immunomodulatory approaches in ERT-naïve settings, especially in CRIM-positive patients, which may in turn improve the overall clinical outcomes by minimizing the development of ADA. The PIMA approach may also be useful for other types of enzyme or factor replacement therapies.

An Immunomodulatory Therapeutic Vaccine Targeting Oligomeric Amyloid-β

2020 ◽  
Vol 77 (4) ◽  
pp. 1639-1653
Author(s):  
Ge Song ◽  
Haiqiang Yang ◽  
Ning Shen ◽  
Phillip Pham ◽  
Breanna Brown ◽  
...  
Keyword(s):  
T Cell ◽  
Spatial Working Memory ◽  
Therapeutic Vaccine ◽  
Cell Epitope ◽  
Amyloid Β ◽  
Epitope Prediction ◽  
Autoimmune Response ◽  
T Cell Epitopes ◽  
Cytokine Expression Profile ◽  
Clinical Benefits

Background: Aging is considered the most important risk factor for Alzheimer’s disease (AD). Recent research supports the theory that immunotherapy targeting the “oligomeric” forms of amyloid-β (Aβ) may halt the progression of AD. However, previous clinical trial of the vaccine against Aβ, called AN1792, was suspended due to cases of meningoencephalitis in patients. Objective: To develop a peptide sensitized dendritic cells (DCs) vaccine that would target oligomer Aβ and prevent an autoimmune response. Methods: Double transgenic APPswe/PS1ΔE9 (Tg) and C57BL/6J control mice were used in this study. Cytokine expression profile detection, characterization of antisera, brain GSK-3β, LC3 expression, and spatial working memory testing before and post-vaccination were obtained. Results: Epitope prediction indicated that E22W42 could generate 13 new T cell epitopes which can strengthen immunity in aged subjects and silence several T cell epitopes of the wild type Aβ. The silenced T cell epitope could help avoid the autoimmune response that was seen in some patients of the AN-1792 vaccine. The E22W42 not only helped sensitize bone marrow-derived DCs for the development of an oligomeric Aβ-specific antibody, but also delayed memory impairment in the APP/PS1 mouse model. Most importantly, this E22W42 peptide will not alter the DC’s natural immunomodulatory properties. Conclusion: The E22W42 vaccine is possibly safer for patients with impaired immune systems. Since there is increasing evidence that oligomeric form of Aβ are the toxic species to neurons, the E22W42 antibody’s specificity for these “oligomeric” Aβ species could provide the opportunity to produce some clinical benefits in AD subjects.

scholarly journals Benchmarking predictions of MHC class I restricted T cell epitopes

2019 ◽  
Author(s):  
Sinu Paul ◽  
Nathan P. Croft ◽  
Anthony W. Purcell ◽  
David C. Tscharke ◽  
Alessandro Sette ◽  
...  
Keyword(s):  
Neural Network ◽  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Computational Prediction ◽  
Prediction Methods ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Prediction Tools ◽  
T Cell Epitope Prediction

AbstractT cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected mice, considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top 0.04% (N = 277) of peptides in the VACV proteome (N = 767,788). These performance metrics provide guidance for immunologists as to which prediction methods to use. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.Author summaryComputational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

scholarly journals Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Syed Nisar Hussain Bukhari ◽  
Amit Jain ◽  
Ehtishamul Haq ◽  
Moaiad Ahmad Khder ◽  
Rahul Neware ◽  
...  
Keyword(s):  
Machine Learning ◽  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Ensemble Model ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Peptide Vaccines ◽  
Data Set ◽  
T Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

scholarly journals Multi-epitope Based Peptide Vaccine Design Using Three Structural Proteins (S, E, and M) of SARS-CoV-2: An In Silico Approach

2020 ◽  
Author(s):  
Arpita Singha Roy ◽  
Mahafujul Islam Quadery Tonmoy ◽  
Atqiya Fariha ◽  
Ithmam Hami ◽  
Ibrahim Khalil Afif ◽  
...  
Keyword(s):  
T Cell ◽  
B Cell ◽  
Peptide Vaccine ◽  
Evolutionary Relationship ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Vaccine Design ◽  
Structural Proteins ◽  
T Cell Epitopes ◽  
Novel Coronavirus

AbstractSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the novel coronavirus responsible for the ongoing pandemic of coronavirus disease (COVID-19). No sustainable treatment option is available so far to tackle such a public health threat. Therefore, designing a suitable vaccine to overcome this hurdle asks for immediate attention. In this study, we targeted for a design of multi-epitope based vaccine using immunoinformatics tools. We considered the structural proteins S, E and, M of SARS-CoV-2, since they facilitate the infection of the virus into host cell and using different bioinformatics tools and servers, we predicted multiple B-cell and T-cell epitopes having potential for the required vaccine design. Phylogenetic analysis provided insight on ancestral molecular changes and molecular evolutionary relationship of S, E, and M proteins. Based on the antigenicity and surface accessibility of these proteins, eight epitopes were selected by various B cell and T cell epitope prediction tools. Molecular docking was executed to interpret the binding interactions of these epitopes and three potential epitopes WTAGAAAYY, YVYSRVKNL, and GTITVEELK were selected for their noticeable higher binding affinity scores −9.1, −7.4, and −7.0 kcal/mol, respectively. Targeted epitopes had 91.09% population coverage worldwide. In summary, we identified three epitopes having the most significant properties of designing the peptide-based vaccine against SARS-CoV-2.

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