A Computational Vaccine Designing Approach for MERS-CoV Infections
Identifieur interne : 000190 ( Pmc/Curation ); précédent : 000189; suivant : 000191A Computational Vaccine Designing Approach for MERS-CoV Infections
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- Immunoinformatics ; 2020.
Abstract
The aim of this study was to use IEDB software to predict the suitable MERS-CoV epitope vaccine against the most known world population alleles through four selecting proteins such as S glycoprotein and envelope protein and their modification sequences after the pandemic spread of MERS-CoV in 2012. IEDB services is one of the computational methods; the output of this study showed that S glycoprotein, envelope (E) protein, and S and E protein modified sequences of MERS-CoV might be considered as a protective immunogenic with high conservancy because they can elect both neutralizing antibodies and T-cell responses when reacting with B-cell, T-helper cell, and cytotoxic T lymphocyte. NetCTL, NetChop, and MHC-NP were used to confirm our results. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover most of the world population in more than 60 geographical regions. According to AllerHunter results, all those selected different protein showed non-allergen; this finding makes this computational vaccine study more desirable for vaccine synthesis.
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DOI: 10.1007/978-1-0716-0389-5_4
PubMed: 32162250
PubMed Central: 7121163
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IEDB services is one of the computational methods; the output of this study showed that S glycoprotein, envelope (E) protein, and S and E protein modified sequences of MERS-CoV might be considered as a protective immunogenic with high conservancy because they can elect both neutralizing antibodies and T-cell responses when reacting with B-cell, T-helper cell, and cytotoxic T lymphocyte. NetCTL, NetChop, and MHC-NP were used to confirm our results. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover most of the world population in more than 60 geographical regions. 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Doytchinova</name></author><author><name sortKey="Flower, Rd" uniqKey="Flower R">RD Flower</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="chapter-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="publisher-id">978-1-0716-0389-5</journal-id><journal-id journal-id-type="doi">10.1007/978-1-0716-0389-5</journal-id><journal-id journal-id-type="nlm-ta">Immunoinformatics</journal-id><journal-title-group><journal-title>Immunoinformatics</journal-title></journal-title-group><isbn publication-format="print">978-1-0716-0388-8</isbn><isbn publication-format="electronic">978-1-0716-0389-5</isbn></journal-meta><article-meta><article-id pub-id-type="pmid">32162250</article-id><article-id pub-id-type="pmc">7121163</article-id><article-id pub-id-type="publisher-id">4</article-id><article-id pub-id-type="doi">10.1007/978-1-0716-0389-5_4</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Computational Vaccine Designing Approach for MERS-CoV Infections</article-title></title-group><contrib-group content-type="book editors"><contrib contrib-type="editor"><name><surname>Tomar</surname><given-names>Namrata</given-names></name><address><email>namrata.tomar@gmail.com</email></address><xref ref-type="aff" rid="Aff2"></xref></contrib><aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.30760.32</institution-id><institution-id institution-id-type="ISNI">0000 0001 2111 8460</institution-id><institution>Department of BioMedical Engineering,</institution><institution>Medical College of Wisconsin,</institution></institution-wrap>Milwaukee, WI USA</aff></contrib-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Ibrahim</surname><given-names>Hiba Siddig</given-names></name><address><email>hibasiddig55@gmail.com</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Kafi</surname><given-names>Shamsoun Khamis</given-names></name><address><email>westnile2017@gmail.com</email></address><xref ref-type="aff" rid="Aff4">4</xref></contrib><aff id="Aff3"><label>3</label>Sudan Diabetic Childhood Center, Khartoum, Sudan</aff><aff id="Aff4"><label>4</label><institution-wrap><institution-id institution-id-type="GRID">grid.449328.0</institution-id><institution>Faculty of Medical Laboratory Science (MLS),</institution><institution>The National Ribat University,</institution></institution-wrap>Khartoum, Sudan</aff></contrib-group><pub-date pub-type="epub"><day>12</day><month>03</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>2131</volume><fpage>39</fpage><lpage>145</lpage><permissions><copyright-statement>© Springer Science+Business Media, LLC, part of Springer Nature 2020</copyright-statement><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p></license></permissions><abstract id="Abs1"><p id="Par1">The aim of this study was to use IEDB software to predict the suitable MERS-CoV epitope vaccine against the most known world population alleles through four selecting proteins such as S glycoprotein and envelope protein and their modification sequences after the pandemic spread of MERS-CoV in 2012. IEDB services is one of the computational methods; the output of this study showed that S glycoprotein, envelope (E) protein, and S and E protein modified sequences of MERS-CoV might be considered as a protective immunogenic with high conservancy because they can elect both neutralizing antibodies and T-cell responses when reacting with B-cell, T-helper cell, and cytotoxic T lymphocyte. NetCTL, NetChop, and MHC-NP were used to confirm our results. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover most of the world population in more than 60 geographical regions. According to AllerHunter results, all those selected different protein showed non-allergen; this finding makes this computational vaccine study more desirable for vaccine synthesis.</p></abstract><kwd-group xml:lang="en"><title>Key words</title><kwd>Middle East respiratory syndrome coronavirus</kwd><kwd>Severe acute respiratory syndrome coronavirus</kwd><kwd>Federal Drug Administration</kwd><kwd>Immuno epitope database</kwd><kwd>FAO</kwd><kwd>AllerHunter</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Springer Science+Business Media, LLC, part of Springer Nature 2020</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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