Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus
Background: The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelia...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Biomedical Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2319417020300068 |
| _version_ | 1811212558503247872 |
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| author | Fisayo A. Olotu Mahmoud E.S. Soliman |
| author_facet | Fisayo A. Olotu Mahmoud E.S. Soliman |
| author_sort | Fisayo A. Olotu |
| collection | DOAJ |
| description | Background: The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity. Methods: Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes. Results: Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability. Conclusion: This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases. |
| first_indexed | 2024-04-12T05:31:04Z |
| format | Article |
| id | doaj.art-24e5c332c2ee43e7b953bee07e1024aa |
| institution | Directory Open Access Journal |
| issn | 2319-4170 |
| language | English |
| last_indexed | 2024-04-12T05:31:04Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomedical Journal |
| spelling | doaj.art-24e5c332c2ee43e7b953bee07e1024aa2022-12-22T03:46:04ZengElsevierBiomedical Journal2319-41702021-06-01443317337Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virusFisayo A. Olotu0Mahmoud E.S. Soliman1Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South AfricaCorresponding author. Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 3629, South Africa.; Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South AfricaBackground: The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity. Methods: Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes. Results: Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability. Conclusion: This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.http://www.sciencedirect.com/science/article/pii/S2319417020300068ImmunoinformaticsEpstein-bar virusEpitopesMajor histocompatibility complex 1Antigenic proteinsPeptide-based vaccine |
| spellingShingle | Fisayo A. Olotu Mahmoud E.S. Soliman Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus Biomedical Journal Immunoinformatics Epstein-bar virus Epitopes Major histocompatibility complex 1 Antigenic proteins Peptide-based vaccine |
| title | Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus |
| title_full | Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus |
| title_fullStr | Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus |
| title_full_unstemmed | Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus |
| title_short | Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus |
| title_sort | immunoinformatics prediction of potential b cell and t cell epitopes as effective vaccine candidates for eliciting immunogenic responses against epstein barr virus |
| topic | Immunoinformatics Epstein-bar virus Epitopes Major histocompatibility complex 1 Antigenic proteins Peptide-based vaccine |
| url | http://www.sciencedirect.com/science/article/pii/S2319417020300068 |
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