Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV
Identifieur interne : 000F11 ( Ncbi/Curation ); précédent : 000F10; suivant : 000F12Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV
Auteurs : Mohammad Tuhin Ali [Bangladesh] ; Mohammed Monzur Morshed [Bangladesh] ; Md. Amran Gazi [Bangladesh] ; Md. Abu Musa [Bangladesh] ; Md Golam Kibria [Bangladesh] ; Md Jashim Uddin [Bangladesh] ; Md. Anik Ashfaq Khan [Bangladesh] ; Shihab Hasan [Australie]Source :
- Bioinformation [ 0973-8894 ] ; 2014.
Abstract
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) belongs to the coronaviridae family. In spite of several outbreaks in
the very recent years, no vaccine against this deadly virus is developed yet. In this study, the receptor binding domain (RBD) of
Spike (S) glycoprotein of MERS-CoV was analyzed through Computational Immunology approach to identify the antigenic
determinants (epitopes). In order to do so, the sequences of S glycoprotein that belong to different geographical regions were
aligned to observe the conservancy of MERS-CoV RBD. The immune parameters of this region were determined using different
Url:
DOI: 10.6026/97320630010533
PubMed: 25258490
PubMed Central: 4166774
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<author><name sortKey="Khan, Md Anik Ashfaq" sort="Khan, Md Anik Ashfaq" uniqKey="Khan M" first="Md. Anik Ashfaq" last="Khan">Md. Anik Ashfaq Khan</name>
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<author><name sortKey="Hasan, Shihab" sort="Hasan, Shihab" uniqKey="Hasan S" first="Shihab" last="Hasan">Shihab Hasan</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV</title>
<author><name sortKey="Ali, Mohammad Tuhin" sort="Ali, Mohammad Tuhin" uniqKey="Ali M" first="Mohammad Tuhin" last="Ali">Mohammad Tuhin Ali</name>
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<country xml:lang="fr">Bangladesh</country>
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<wicri:noRegion>University of Dhaka</wicri:noRegion>
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<author><name sortKey="Morshed, Mohammed Monzur" sort="Morshed, Mohammed Monzur" uniqKey="Morshed M" first="Mohammed Monzur" last="Morshed">Mohammed Monzur Morshed</name>
<affiliation wicri:level="1"><nlm:aff id="A1">Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</nlm:aff>
<country xml:lang="fr">Bangladesh</country>
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<author><name sortKey="Gazi, Md Amran" sort="Gazi, Md Amran" uniqKey="Gazi M" first="Md. Amran" last="Gazi">Md. Amran Gazi</name>
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<author><name sortKey="Musa, Md Abu" sort="Musa, Md Abu" uniqKey="Musa M" first="Md. Abu" last="Musa">Md. Abu Musa</name>
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<author><name sortKey="Kibria, Md Golam" sort="Kibria, Md Golam" uniqKey="Kibria M" first="Md Golam" last="Kibria">Md Golam Kibria</name>
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<author><name sortKey="Uddin, Md Jashim" sort="Uddin, Md Jashim" uniqKey="Uddin M" first="Md Jashim" last="Uddin">Md Jashim Uddin</name>
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<author><name sortKey="Khan, Md Anik Ashfaq" sort="Khan, Md Anik Ashfaq" uniqKey="Khan M" first="Md. Anik Ashfaq" last="Khan">Md. Anik Ashfaq Khan</name>
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<wicri:noRegion>Dhaka</wicri:noRegion>
</affiliation>
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<author><name sortKey="Hasan, Shihab" sort="Hasan, Shihab" uniqKey="Hasan S" first="Shihab" last="Hasan">Shihab Hasan</name>
<affiliation wicri:level="1"><nlm:aff id="A4">Bioinformatics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Bioinformatics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland</wicri:regionArea>
<wicri:noRegion>Queensland</wicri:noRegion>
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<affiliation wicri:level="1"><nlm:aff id="A5">School of Medicine, The University of Queensland (UQ), Brisbane, Queensland, Australia</nlm:aff>
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<series><title level="j">Bioinformation</title>
<idno type="ISSN">0973-8894</idno>
<idno type="eISSN">0973-2063</idno>
<imprint><date when="2014">2014</date>
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<front><div type="abstract" xml:lang="en"><p>Middle East Respiratory Syndrome Coronavirus (MERS-CoV) belongs to the coronaviridae family. In spite of several outbreaks in
the very recent years, no vaccine against this deadly virus is developed yet. In this study, the receptor binding domain (RBD) of
Spike (S) glycoprotein of MERS-CoV was analyzed through Computational Immunology approach to identify the antigenic
determinants (epitopes). In order to do so, the sequences of S glycoprotein that belong to different geographical regions were
aligned to observe the conservancy of MERS-CoV RBD. The immune parameters of this region were determined using different <italic>in
silico</italic>
tools and Immune Epitope Database (IEDB). Molecular docking study was also employed to check the affinity of the potential
epitope towards the binding cleft of the specific HLA allele. The N-terminus RBD (S367-S606) of S glycoprotein was found to be
conserved among all the available strains of MERS-CoV. Based on the lower IC<sub>50</sub>
value, a total of eight potential T-cell epitopes and
19 major histocompatibility complex (MHC) class-I alleles were identified for this conserved region. A 9-mer epitope CYSSLILDY
displayed interactions with the maximum number of MHC class-I molecules and projected the highest peak in the B-cell
antigenicity plot which concludes that it could be a better choice for designing an epitope based peptide vaccine against MERSCoV
considering that it must undergo further <italic>in vitro</italic>
and <italic>in vivo</italic>
experiments. Moreover, in molecular docking study, this epitope
was found to have a significant binding affinity of -8.5 kcal/mol towards the binding cleft of the HLA-C*12:03 molecule.</p>
</div>
</front>
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</listBibl>
</div1>
</back>
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