Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV
Identifieur interne : 000B27 ( Pmc/Corpus ); précédent : 000B26; suivant : 000B28Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV
Auteurs : Mohammad Tuhin Ali ; Mohammed Monzur Morshed ; Md. Amran Gazi ; Md. Abu Musa ; Md Golam Kibria ; Md Jashim Uddin ; Md. Anik Ashfaq Khan ; Shihab HasanSource :
- 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
Links to Exploration step
PMC:4166774Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">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><affiliation><nlm:aff id="A1">Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Morshed, Mohammed Monzur" sort="Morshed, Mohammed Monzur" uniqKey="Morshed M" first="Mohammed Monzur" last="Morshed">Mohammed Monzur Morshed</name><affiliation><nlm:aff id="A1">Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Gazi, Md Amran" sort="Gazi, Md Amran" uniqKey="Gazi M" first="Md. Amran" last="Gazi">Md. Amran Gazi</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Musa, Md Abu" sort="Musa, Md Abu" uniqKey="Musa M" first="Md. Abu" last="Musa">Md. Abu Musa</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Kibria, Md Golam" sort="Kibria, Md Golam" uniqKey="Kibria M" first="Md Golam" last="Kibria">Md Golam Kibria</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Uddin, Md Jashim" sort="Uddin, Md Jashim" uniqKey="Uddin M" first="Md Jashim" last="Uddin">Md Jashim Uddin</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><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><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Hasan, Shihab" sort="Hasan, Shihab" uniqKey="Hasan S" first="Shihab" last="Hasan">Shihab Hasan</name><affiliation><nlm:aff id="A4">Bioinformatics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="A5">School of Medicine, The University of Queensland (UQ), Brisbane, Queensland, Australia</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25258490</idno><idno type="pmc">4166774</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166774</idno><idno type="RBID">PMC:4166774</idno><idno type="doi">10.6026/97320630010533</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000B27</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B27</idno></publicationStmt><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><affiliation><nlm:aff id="A1">Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Morshed, Mohammed Monzur" sort="Morshed, Mohammed Monzur" uniqKey="Morshed M" first="Mohammed Monzur" last="Morshed">Mohammed Monzur Morshed</name><affiliation><nlm:aff id="A1">Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Gazi, Md Amran" sort="Gazi, Md Amran" uniqKey="Gazi M" first="Md. Amran" last="Gazi">Md. Amran Gazi</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Musa, Md Abu" sort="Musa, Md Abu" uniqKey="Musa M" first="Md. Abu" last="Musa">Md. Abu Musa</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Kibria, Md Golam" sort="Kibria, Md Golam" uniqKey="Kibria M" first="Md Golam" last="Kibria">Md Golam Kibria</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Uddin, Md Jashim" sort="Uddin, Md Jashim" uniqKey="Uddin M" first="Md Jashim" last="Uddin">Md Jashim Uddin</name><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><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><affiliation><nlm:aff id="A2">Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</nlm:aff></affiliation></author><author><name sortKey="Hasan, Shihab" sort="Hasan, Shihab" uniqKey="Hasan S" first="Shihab" last="Hasan">Shihab Hasan</name><affiliation><nlm:aff id="A4">Bioinformatics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia</nlm:aff></affiliation><affiliation><nlm:aff id="A5">School of Medicine, The University of Queensland (UQ), Brisbane, Queensland, Australia</nlm:aff></affiliation></author></analytic><series><title level="j">Bioinformation</title><idno type="ISSN">0973-8894</idno><idno type="eISSN">0973-2063</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><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><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Coleman, Cm" uniqKey="Coleman C">CM Coleman</name></author><author><name sortKey="Frieman, Mb" uniqKey="Frieman M">MB Frieman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Assiri, A" uniqKey="Assiri A">A Assiri</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Petrovsky, N" uniqKey="Petrovsky N">N Petrovsky</name></author><author><name sortKey="Brusic, V" uniqKey="Brusic V">V Brusic</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rota, Pa" uniqKey="Rota P">PA Rota</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Spaan, W" uniqKey="Spaan W">W Spaan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Raj, Vs" uniqKey="Raj V">VS Raj</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jiang, S" uniqKey="Jiang S">S Jiang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Weiss, Sr" uniqKey="Weiss S">SR Weiss</name></author><author><name sortKey="Navas Martin, S" uniqKey="Navas Martin S">S Navas-Martin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Du, L" uniqKey="Du L">L Du</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, N" uniqKey="Wang N">N Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Du, L" uniqKey="Du L">L Du</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sievers, F" uniqKey="Sievers F">F Sievers</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Larsen, Mv" uniqKey="Larsen M">MV Larsen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Larsen, Mv" uniqKey="Larsen M">MV Larsen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peters, B" uniqKey="Peters B">B Peters</name></author><author><name sortKey="Sette, A" uniqKey="Sette A">A Sette</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tenzer, S" uniqKey="Tenzer S">S Tenzer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peters, B" uniqKey="Peters B">B Peters</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bui, H H" uniqKey="Bui H">H-H Bui</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Singh, H" uniqKey="Singh H">H Singh</name></author><author><name sortKey="Raghava, G" uniqKey="Raghava G">G Raghava</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rammensee, H G" uniqKey="Rammensee H">H-G Rammensee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Morris, Gm" uniqKey="Morris G">GM Morris</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Trott, O" uniqKey="Trott O">O Trott</name></author><author><name sortKey="Olson, Aj" uniqKey="Olson A">AJ Olson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kolaskar, A" uniqKey="Kolaskar A">A Kolaskar</name></author><author><name sortKey="Tongaonkar, Pc" uniqKey="Tongaonkar P">PC Tongaonkar</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhou, J" uniqKey="Zhou J">J Zhou</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Bioinformation</journal-id><journal-id journal-id-type="iso-abbrev">Bioinformation</journal-id><journal-id journal-id-type="publisher-id">Bioinformation</journal-id><journal-title-group><journal-title>Bioinformation</journal-title></journal-title-group><issn pub-type="ppub">0973-8894</issn><issn pub-type="epub">0973-2063</issn><publisher><publisher-name>Biomedical Informatics</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25258490</article-id><article-id pub-id-type="pmc">4166774</article-id><article-id pub-id-type="publisher-id">97320630010533</article-id><article-id pub-id-type="doi">10.6026/97320630010533</article-id><article-categories><subj-group subj-group-type="heading"><subject>Hypothesis</subject></subj-group></article-categories><title-group><article-title>Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>ali</surname><given-names>Mohammad Tuhin</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="corresp" rid="COR1">*</xref></contrib><contrib contrib-type="author"><name><surname>Morshed</surname><given-names>Mohammed Monzur</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Gazi</surname><given-names>Md. Amran</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Musa</surname><given-names>Md. Abu</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Kibria</surname><given-names>Md Golam</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Uddin</surname><given-names>Md Jashim</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Khan</surname><given-names>Md. Anik Ashfaq</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Hasan</surname><given-names>Shihab</given-names></name><xref ref-type="aff" rid="A4">4</xref><xref ref-type="aff" rid="A5">5</xref><xref ref-type="corresp" rid="COR2">*</xref></contrib><aff id="A1"><label>1</label>Department of Biochemistry and Molecular Biology, University of Dhaka, Bangladesh</aff><aff id="A2"><label>2</label>Laboratory Science Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh</aff><aff id="A3"><label>3</label>Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet, Bangladesh</aff><aff id="A4"><label>4</label>Bioinformatics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia</aff><aff id="A5"><label>5</label>School of Medicine, The University of Queensland (UQ), Brisbane, Queensland, Australia</aff></contrib-group><author-notes><corresp id="COR1"><label>*</label>Mohammad Tuhin Ali: <email>m.tuhinali@gmail.com</email> Phone: +8801747687885</corresp><corresp id="COR2"><label>*</label>Shihab Hasan: <email>shihab.hasan@qimrberghofer.edu.au</email> Phone: +61733620468, +61466097724</corresp></author-notes><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>30</day><month>8</month><year>2014</year></pub-date><volume>10</volume><issue>8</issue><fpage>533</fpage><lpage>538</lpage><history><date date-type="received"><day>23</day><month>6</month><year>2014</year></date><date date-type="rev-recd"><day>07</day><month>7</month><year>2014</year></date><date date-type="accepted"><day>08</day><month>7</month><year>2014</year></date></history><permissions><copyright-statement>© 2014 Biomedical Informatics</copyright-statement><copyright-year>2014</copyright-year><license license-type="open-access"><license-p>This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium,
for non-commercial purposes, provided the original author and source are credited.</license-p></license></permissions><abstract><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></abstract><kwd-group><kwd>Epitope</kwd><kwd>HLA ligand</kwd><kwd>MERS-CoV</kwd><kwd>Receptor Binding Domain</kwd><kwd>Spike glycoprotein</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>Background</title><p>MERS-CoV is an enveloped positive sense single stranded
RNA virus from betacoronavirus genus. Its natural reservoir is
still unidentified but similarity with other viruses suggested
that it is of bat origin [<xref rid="R01" ref-type="bibr">1</xref>]. Infection with MERS-CoV is
characterized mostly by severe acute respiratory syndrome
(SARS). Clinical symptoms also include fever, fever with chills
or rigours, cough, shortness of breath and in some cases renal
failure. Gastrointestinal symptoms were also found in infected
patients including diarrhoea, vomiting and abdominal pain
[<xref rid="R02" ref-type="bibr">2</xref>].
The first official outbreak was recorded in Saudi Arabia on
September 2012. Recurrent infections of MERS-CoV has been
reported from 2012 to 2014 in Jordan, Kuwait, Oman, Qatar,
Saudi Arabia (KSA), United Arab Emirates (UAE), Yemen,
Egypt, Tunisia, France, Germany, Greece, Italy, and United
Kingdom. According to the World Health Organization
(WHO), till date a total of 536 laboratory-confirmed cases of
MERS-CoV infections have been identified, 145 of which
resulted in death (<ext-link ext-link-type="uri" xlink:href="http://www.who.int/csr/disease/coronavirusinfections/archive_updates/en">http://www.who.int /csr/disease
/coronavirus infections/archive_updates/en</ext-link>). Though it has
not hit the world population in a massive scale yet, its
progressive invasiveness with concerning fatality rate raises
the demand of therapeutic solutions or vaccination on an
urgent basis.</p><p>The field of Computational Immunology is developing
rapidly. Current tools enable us to predict potential epitopes
from large protein antigens encoded by viral genomes.
Identification of B- and T-cell epitopes and their respective
MHC alleles is the crucial step for the very initial screening
process of immunoinformatics approach [<xref rid="R03" ref-type="bibr">3</xref>].</p><p>Coronavirus possess a non-structural replicase polyprotein
and several structural proteins including spike (S), envelope
(E), membrane (M) and nucleocapsid (N) proteins [<xref rid="R04" ref-type="bibr">4</xref>,
<xref rid="R05" ref-type="bibr">5</xref>]. Like
other coronaviruses, the host cell recognition of MERS-CoV by
CD26 receptor is mediated by its surface anchored S
glycoprotein [<xref rid="R06" ref-type="bibr">6</xref>,
<xref rid="R07" ref-type="bibr">7</xref>]. This S glycoprotein has S1 and S2 subunits.
S1 subunit contains RBD which mediates initial host cell
recognition whereas S2 subunit mediates membrane fusion
[<xref rid="R08" ref-type="bibr">8</xref>,
<xref rid="R09" ref-type="bibr">9</xref>].</p><p>Several recent studies have revealed that the N-terminal 367-
606 amino acid region of S glycoprotein of MERS-CoV is
bound with human CD26 and produces significant immune
response. This phenomenon suggests that the receptor binding
capacity of MERS-CoV lies in this particular region of S
glycoprotein [<xref rid="R10" ref-type="bibr">10</xref>,
<xref rid="R11" ref-type="bibr">11</xref>]. Considering these facts, our approach
was to choose this particular region of S glycoprotein for the
prediction and identification of the potential T- and B-cell
epitopes in this computational study.</p></sec><sec sec-type="methods" id="s2"><title>Methodology</title><sec id="s2a"><title><italic>Retrieving protein sequence</italic>:</title><p>The sequences of S glycoprotein were retrieved from two
databases: NCBI (National Center for Biotechnology
Information) (<ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov/">http://www.ncbi.nlm.nih.gov/</ext-link>) and uniprot
(<ext-link ext-link-type="uri" xlink:href="http://www.uniprot.org/">http://www.uniprot.org/</ext-link>). These sequences belong to
diverse demographic distributions like Saudi Arabia, England,
Qatar, Spain, Germany, Jordan, and UAE with time ranges
from 2012 to 2013.</p></sec><sec id="s2b"><title><italic>Multiple sequence alignment</italic>:</title><p>The Clustal Omega software (version 1.2.1) was utilized to
generate multiple sequence alignment for retrieved sequences
which was the basis for determining the conservancy of MERS
CoV RBD [<xref rid="R12" ref-type="bibr">12</xref>].</p></sec><sec id="s2c"><title><italic>Immunogenicity of conserved peptide</italic>:</title><p>Different tools available from the IEDB analysis resource
(<ext-link ext-link-type="uri" xlink:href="http://tools.immuneepitope.org/main/index.html">http://tools.immuneepitope.org/main/index.html</ext-link>) were
used to evaluate the immunogenicity of MERS-CoV RBD. The
NetCTL prediction method was utilized for identifying T-cell
epitopes from this region [<xref rid="R13" ref-type="bibr">13</xref>,
<xref rid="R14" ref-type="bibr">14</xref>]. The MHC class-I binding
prediction tool was used to identify MHC class-I alleles for the
final set of T-cell epitopes based on Stabilized Matrix Method
(SMM) [<xref rid="R15" ref-type="bibr">15</xref>].</p><p>“Proteasomal cleavage/TAP transport/MHC class I combined
predictor” was utilized for determining the overall score for
each peptide's intrinsic capability to be considered as a T cell
epitope (<ext-link ext-link-type="uri" xlink:href="http://tools.immuneepitope.org/processing/">http://tools.immuneepitope.org/processing/</ext-link>)
[<xref rid="R15" ref-type="bibr">15</xref>–<xref rid="R17" ref-type="bibr">17</xref>].
The epitope conservancy analysis tool was utilized to
calculate the conservancy of the final set of epitopes within the
sequences of S glycoprotein [<xref rid="R18" ref-type="bibr">18</xref>]. In addition to this, The
ProPred1 and SYFPEITHI databases were also employed to
identify the anchoring residues of the epitopes [<xref rid="R19" ref-type="bibr">19</xref>,
<xref rid="R20" ref-type="bibr">20</xref>].</p></sec><sec id="s2d"><title><italic>3D structures of the predicted epitope and HLA-C*12:03</italic>:</title><p>The 3D structure of MHC class-I molecule (HLA-C*12:03) was
retrieved from the Protein Data Bank (PDB) (HLA-C*12:03;
PDB ID: 2FSE) database (<ext-link ext-link-type="uri" xlink:href="http://www.rcsb.org/pdb/home/home.do">http://www.rcsb.org /pdb/home/
home.do</ext-link>). The 3D structure of the best epitope CYSSLILDY
(see discussion section) was designed by using PEPstr peptide
tertiary structure prediction server (<ext-link ext-link-type="uri" xlink:href="http://www.imtech.res.in/raghava/pepstr/">http://www.imtech.res.in
/raghava/pepstr/</ext-link>).</p></sec><sec id="s2e"><title><italic>Assessment of HLA-epitope interaction by molecular docking study</italic>:</title><p>The AutoDock tools (ADT) from the MGL software package
(version 1.5.6) and AutoDock vina (Vina, version 4.2) were
used for molecular docking study [<xref rid="R21" ref-type="bibr">21</xref>,
<xref rid="R22" ref-type="bibr">22</xref>]. At the beginning of
the docking study, both the 3D structures of protein (HLAC*
12:03) and ligand (CYSSLILDY) were opened in ADT. For
the epitope to be bound at the binding groove of HLA-C*12:03,
the centre grid box parameter was set at 22.474, 5.416, 34.437
Å in x, y and z directions respectively with 1.0 Å spacing. The
points were set at 40, 38, 52 Å in x, y and z directions
respectively. AutoDock tool was utilized to set the above
parameters whereas AutoDock vina was applied for
conducting docking experiment. Output files were visualized
by PyMOL molecular visualization system (version: 1.5.0.3).
Along with this, the Immunodominant Determinant of human
type-2 collagen (PDB ID: 2FSE) was used as ligand to run
positive control maintaining all the parameters as same as
initial docking of this study.</p></sec><sec id="s2f"><title><italic>Prediction of B-cell epitope</italic>:</title><p>The B-cell epitope prediction tool from IEDB was used to
predict the B-cell antigenicity of MERS-CoV RBD. The
Kolaskar and Tongaonkar method was employed for this
prediction which can predict antigenic determinants with
approximately 75% accuracy [<xref rid="R23" ref-type="bibr">23</xref>].</p></sec></sec><sec id="s3"><title>Results</title><sec id="s3a"><title><italic>Conservancy of MERS-CoV RBD</italic>:</title><p>An illustration of the MERS-CoV RBD in complex with the
human CD26 protein (DPP4) is given in <xref ref-type="fig" rid="F1">Figure 1</xref>. A total of 54
sequences of S glycoprotein were subjected to multiple
sequence alignment. From the output of multiple sequence
alignment of S glycoprotein, it was found that a peptide region
of 367-606 amino acids remained conserved among all the
given sequences (<xref ref-type="fig" rid="F2">Figure 2A</xref>). Thus the above finding gives a
convincing affirmation that MERS-CoV RBD is a well
conserved region.</p></sec><sec id="s3b"><title><italic>Immunogenicity of MERS-CoV RBD</italic>:</title><p>235 overlapping CTL peptides and 79 possible MHC-1 alleles
were predicted by NetCTL and MHC class-I binding
prediction tool, respectively. The final set of epitopes and their
respective MHC class-I allele was generated on the basis of a
total score ≥ 1 and the “half maximal inhibitory concentration
(IC<sub>50</sub>)” value ≤ 100. This final set includes only eight T-cell
epitopes and a total of 19 MHC class-I alleles (<xref ref-type="fig" rid="F2">Figure 2A</xref> &
<xref ref-type="fig" rid="F2">Figure 2B</xref>). A graphical presentation of the result predicted by
the NetCTL prediction tool is given in <xref ref-type="fig" rid="F3">Figure 3</xref>. The
immunogenicity of final epitopes was also determined in terms
of proteasome score, TAP score, MHC-I binding score and
processing score. All of the eight T-cell epitopes demonstrated
100% sequence conservancy within the given sequences of S
glycoprotein which is summarized in <xref ref-type="supplementary-material" rid="SD1">Table 1</xref> (See
supplementary material).</p></sec><sec id="s3c"><title><italic>HLA-epitope interactions</italic>:</title><p>The designed epitope (CYSSLILDY) and experimental epitope
(immunodominant determinant of human type-2 collagen;
PDB ID: 2FSE) are displayed in <xref ref-type="fig" rid="F3">Figure 3</xref>. AutoDock vina
predicted the best conformer of CYSSLILDY epitope based on
the binding energy in kcal/mol unit. In our study, the best
conformer of this epitope had the binding energy of -8.3
kcal/mol which was very similar with that of the positive
control experiment (-7.5 kcal/mol). Among 9 residues in the
epitope, only cysteine, serine and tyrosine (first, fourth and
ninth residue, respectively) showed the anchoring capacity
and other six residues remained at the binding groove of HLAC *
12:03 molecule. For both the designed and experimental
epitopes at the binding groove of HLA-C*12:03, a comparative
analysis of their binding pattern is illustrated in
<xref ref-type="fig" rid="F4">Figure 4</xref>.</p></sec><sec id="s3d"><title><italic>B-cell antigenicity</italic>:</title><p>The graphical depiction of B-cell antigenicity of MERS-CoV
RBD is in <xref ref-type="fig" rid="F5">Figure 5</xref>. Average antigenic propensity for this
region is 1.050. Twelve antigenic determinants were found
from this region which is presented in <xref ref-type="supplementary-material" rid="SD1">Table 2</xref> (See
supplementary material).</p></sec></sec><sec id="s4"><title>Discussion</title><p>In recent years, <italic>in silico</italic> approach has become handy in vaccine
designing against deadly entities as it provides clue to select
target protein sequence. Till date, there is no established
therapeutic solution or vaccination against MERS-CoV
infection. Several reports in recent years reveal that MERS-CoV
produces strong T-cell mediated immune responses in human
body, inspiring us to design T-cell epitope based peptide
vaccine [<xref rid="R24" ref-type="bibr">24</xref>].</p><p>To be considered as a potent epitope, a peptide must contain
several key properties like well conservancy, T- or B-cell
processivity and good binding affinity for MHC alleles. In our
study, the values for above mentioned parameters were found
in favour of the finally chosen epitopes.</p><p>In this study we used NetCTL prediction tool for the primary
screening of the RBD of S glycoprotein of MERS-CoV for T-cell
epitope prediction which is based on neural network
architecture. It predicts the epitopes on the basis of processing
of peptide in vivo. It gives a total score for each epitopes by
using an algorithm integrating MHC-I binding, Transporter of
Antigenic peptides (TAP) transport efficiency and proteasomal
cleavage prediction [<xref rid="R13" ref-type="bibr">13</xref>,
<xref rid="R14" ref-type="bibr">14</xref>].</p><p>Among eight T-cell epitopes, CYSSLILDY was the most
successful because it was found to have the maximum
interactions with different MHC class-I alleles which is a
principle prerequisite for a potential epitope. In addition to
this, it was found that the peptide region of 408-452 amino acids
“NYNLTKLLSLFSVNDFTCSQISPAAIASNCYSSLILDYFSYPL
S” of S glycoprotein of MERS-CoV shows the highest B-cell
antigenicity peak in antigenicity plot. Most importantly, we
observed that the best successful T-cell epitope CYSSLILDY is
also a part of the region which shows highest B-cell
antigenicity peak, suggesting that it may stand as a competent
choice to be a universal vaccine component against MERSCoV,
the rapidly growing health concern.</p></sec><sec id="s5"><title>Conclusion</title><p>The obtained results which identified the epitopes cover a
significant number of strains with decent population coverage.
To exert cellular and humoral immunity, the computer aided
result has been found potential and expected to mount
immune response upon further in vivo and in vitro validation.
The step by step analysis and progression for maximum
possible MHC coverage provides significant primary screening
result against this fatal virus.</p></sec><sec id="s6"><title>Conflict of interest</title><p>The authors declare that there is no conflict of interests.</p></sec><sec sec-type="supplementary-material"><title>Supplementary material</title><supplementary-material content-type="local-data" id="SD1"><caption><title>Data 1</title></caption><media mimetype="application" mime-subtype="pdf" xlink:href="97320630010533S1.pdf" xlink:type="simple" id="d35e464" position="anchor"></media></supplementary-material></sec></body><back><fn-group><fn id="FN1" fn-type="other"><p><bold>Citation:</bold>Tuhin ali <italic>et al</italic>, Bioinformation 10(8): 533-538 (2014)</p></fn></fn-group><ref-list><title>References</title><ref id="R01"><label>1</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Coleman</surname><given-names>CM</given-names></name><name><surname>Frieman</surname><given-names>MB</given-names></name></person-group><source>PLoS 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host cell surface receptor CD26 (PDB ID: 4KR0). The molecular
visualization software PyMol (version 1.5.0.3) was utilized to
produce this figure. The aquamarine surface represents the
MERS-CoV RBD whereas the green surface represents CD26.</p></caption><graphic xlink:href="97320630010533F1"></graphic></fig><fig id="F2" position="float"><label>Figure 2</label><caption><p>(A) Each potential T- cell epitope of the conserved
region is identified with a blue underline. Their respective
residual position and total score predicted by NetCTL
prediction tool are also provided. (B) Each potential epitope
and their corresponding HLA alleles are represented. Among
the epitopes, CYSSLILDY interacts with the highest number of
11 alleles.</p></caption><graphic xlink:href="97320630010533F2"></graphic></fig><fig id="F3" position="float"><label>Figure 3</label><caption><p>The T-cell epitopes predicted by NetCTL prediction
tool. Most of the epitopes failed to cross threshold level (1.0).
Green coloured sharp points indicate the epitopes that crossed
the threshold level. In this graph, x-axis represents the residue
positions of predicted epitope whereas y-axis represents their
score.</p></caption><graphic xlink:href="97320630010533F3"></graphic></fig><fig id="F4" position="float"><label>Figure 4</label><caption><p>Interactions between the HLA C*1203 allele and the
epitopes. (A) Experimental epitope (Immuno dominant
determinant of human type-2 collagen) bound at the binding
groove of HLA C*1203 allele. (B) Designed epitope
CYSSLILDY bound at the binding groove of HLA C*1203
allele. Cartoon represents the HLA C*1203 allele whereas sticks
represent the epitopes.</p></caption><graphic xlink:href="97320630010533F4"></graphic></fig><fig id="F5" position="float"><label>Figure 5</label><caption><p>B-cell anteginic propensity of the RBD predicted by
the Kolaskar and Tongaonkar prediction method. Twelve
potential B-cell antigenic regions are represented by the yellow
colour whereas the regions represented by the green colour
failed to cross threshold level of 1.0.</p></caption><graphic xlink:href="97320630010533F5"></graphic></fig></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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