Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets
A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective...
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Published in | Vaccines (Basel) Vol. 8; no. 2; p. 290 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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09.06.2020
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ISSN | 2076-393X 2076-393X |
DOI | 10.3390/vaccines8020290 |
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Abstract | A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks. |
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AbstractList | A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks. A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks.A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks. |
Author | Tworowski, Dmitry Mukherjee, Sumit Frenkel-Morgenstern, Milana Detroja, Rajesh Mukherjee, Sunanda Biswas |
AuthorAffiliation | Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel; sumit.mukherjee@biu.ac.il (S.M.); dmitwor@gmail.com (D.T.); rajesh.detroja@biu.ac.il (R.D.); mukhers1@biu.ac.il (S.B.M.) |
AuthorAffiliation_xml | – name: Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel; sumit.mukherjee@biu.ac.il (S.M.); dmitwor@gmail.com (D.T.); rajesh.detroja@biu.ac.il (R.D.); mukhers1@biu.ac.il (S.B.M.) |
Author_xml | – sequence: 1 givenname: Sumit orcidid: 0000-0001-9807-7994 surname: Mukherjee fullname: Mukherjee, Sumit – sequence: 2 givenname: Dmitry surname: Tworowski fullname: Tworowski, Dmitry – sequence: 3 givenname: Rajesh surname: Detroja fullname: Detroja, Rajesh – sequence: 4 givenname: Sunanda Biswas surname: Mukherjee fullname: Mukherjee, Sunanda Biswas – sequence: 5 givenname: Milana surname: Frenkel-Morgenstern fullname: Frenkel-Morgenstern, Milana |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32526960$$D View this record in MEDLINE/PubMed |
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Keywords | COVID-19 SARS-CoV-2 vaccine target HLA immunodominant epitope |
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SubjectTerms | Allergens Antibodies Antigens Cell-mediated immunity Computer applications Coronaviruses COVID-19 Epidemics Epitopes HLA Immunodominance immunodominant epitope Immunotherapy Ligands Lymphocytes T Machine learning Major histocompatibility complex Middle East respiratory syndrome Molecular docking Pandemics Pathogens Peptide libraries Peptides Population Proteins Proteomes Public health SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Structural analysis vaccine target Vaccines Viral diseases |
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Title | Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets |
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