In silico identification of vaccine targets for 2019-nCoV
Identifieur interne : 000420 ( Pmc/Checkpoint ); précédent : 000419; suivant : 000421In silico identification of vaccine targets for 2019-nCoV
Auteurs : Chloe Hyun-Jung Lee [Royaume-Uni] ; Hashem Koohy [Royaume-Uni]Source :
- F1000Research [ 2046-1402 ] ; 2020.
Abstract
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DOI: 10.12688/f1000research.22507.1
PubMed: 32269766
PubMed Central: 7111504
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en"><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</title><author><name sortKey="Hyun Jung Lee, Chloe" sort="Hyun Jung Lee, Chloe" uniqKey="Hyun Jung Lee C" first="Chloe" last="Hyun-Jung Lee">Chloe Hyun-Jung Lee</name><affiliation wicri:level="4"><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="country">Angleterre</region><region type="comté" nuts="2">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation></author><author><name sortKey="Koohy, Hashem" sort="Koohy, Hashem" uniqKey="Koohy H" first="Hashem" last="Koohy">Hashem Koohy</name><affiliation wicri:level="4"><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="country">Angleterre</region><region type="comté" nuts="2">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32269766</idno><idno type="pmc">7111504</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7111504</idno><idno type="RBID">PMC:7111504</idno><idno type="doi">10.12688/f1000research.22507.1</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">001006</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001006</idno><idno type="wicri:Area/Pmc/Curation">001006</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001006</idno><idno type="wicri:Area/Pmc/Checkpoint">000420</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000420</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main"><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</title><author><name sortKey="Hyun Jung Lee, Chloe" sort="Hyun Jung Lee, Chloe" uniqKey="Hyun Jung Lee C" first="Chloe" last="Hyun-Jung Lee">Chloe Hyun-Jung Lee</name><affiliation wicri:level="4"><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="country">Angleterre</region><region type="comté" nuts="2">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation></author><author><name sortKey="Koohy, Hashem" sort="Koohy, Hashem" uniqKey="Koohy H" first="Hashem" last="Koohy">Hashem Koohy</name><affiliation wicri:level="4"><nlm:aff id="a1">MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="country">Angleterre</region><region type="comté" nuts="2">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation></author></analytic><series><title level="j">F1000Research</title><idno type="eISSN">2046-1402</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><bold>Background:</bold> The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.</p><p><bold>Methods:</bold> The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.</p><p><bold>Results:</bold> We report
<italic>in silico</italic> identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a
<italic>de novo</italic> search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.</p><p><bold>Conclusions:</bold> Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Kock, Ra" uniqKey="Kock R">RA Kock</name></author><author><name sortKey="Karesh, Wb" uniqKey="Karesh W">WB Karesh</name></author><author><name sortKey="Veas, F" uniqKey="Veas F">F Veas</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vita, R" uniqKey="Vita R">R Vita</name></author><author><name sortKey="Mahajan, S" uniqKey="Mahajan S">S Mahajan</name></author><author><name sortKey="Overton, Ja" uniqKey="Overton J">JA Overton</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, Ch" uniqKey="Lee C">CH Lee</name></author><author><name sortKey="Koohy, H" uniqKey="Koohy H">H Koohy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pogorelyy, Mv" uniqKey="Pogorelyy M">MV Pogorelyy</name></author><author><name sortKey="Fedorova, Ad" uniqKey="Fedorova A">AD Fedorova</name></author><author><name sortKey="Mclaren, Je" uniqKey="Mclaren J">JE McLaren</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jurtz, V" uniqKey="Jurtz V">V Jurtz</name></author><author><name sortKey="Paul, S" uniqKey="Paul S">S Paul</name></author><author><name sortKey="Andreatta, M" uniqKey="Andreatta M">M Andreatta</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Karapetyan, Ar" uniqKey="Karapetyan A">AR Karapetyan</name></author><author><name sortKey="Chaipan, C" uniqKey="Chaipan C">C Chaipan</name></author><author><name sortKey="Winkelbach, K" uniqKey="Winkelbach K">K Winkelbach</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, Sf" uniqKey="Ahmed S">SF Ahmed</name></author><author><name sortKey="Quadeer, Aa" uniqKey="Quadeer A">AA Quadeer</name></author><author><name sortKey="Mckay, Mr" uniqKey="Mckay M">MR McKay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, Ch" uniqKey="Lee C">CH Lee</name></author><author><name sortKey="Koohy, H" uniqKey="Koohy H">H Koohy</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">F1000Res</journal-id><journal-id journal-id-type="iso-abbrev">F1000Res</journal-id><journal-id journal-id-type="pmc">F1000Research</journal-id><journal-title-group><journal-title>F1000Research</journal-title></journal-title-group><issn pub-type="epub">2046-1402</issn><publisher><publisher-name>F1000 Research Limited</publisher-name><publisher-loc>London, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32269766</article-id><article-id pub-id-type="pmc">7111504</article-id><article-id pub-id-type="doi">10.12688/f1000research.22507.1</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group><subject>Articles</subject></subj-group></article-categories><title-group><article-title><italic>In silico</italic> identification of vaccine targets for 2019-nCoV</article-title><fn-group content-type="pub-status"><fn><p>[version 1; peer review: 2 approved]</p></fn></fn-group></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hyun-Jung Lee</surname><given-names>Chloe</given-names></name><role content-type="http://credit.casrai.org/">Data Curation</role><role content-type="http://credit.casrai.org/">Formal Analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Resources</role><role content-type="http://credit.casrai.org/">Software</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – Original Draft Preparation</role><role content-type="http://credit.casrai.org/">Writing – Review & Editing</role><xref ref-type="aff" rid="a1">1</xref></contrib><contrib contrib-type="author"><name><surname>Koohy</surname><given-names>Hashem</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data Curation</role><role content-type="http://credit.casrai.org/">Funding Acquisition</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Writing – Original Draft Preparation</role><role content-type="http://credit.casrai.org/">Writing – Review & Editing</role><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3640-7043</contrib-id><xref ref-type="corresp" rid="c1">a</xref><xref ref-type="aff" rid="a1">1</xref></contrib><aff id="a1"><label>1</label>MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK</aff></contrib-group><author-notes><corresp id="c1"><label>a</label><email xlink:href="mailto:hashem.koohy@rdm.ox.ac.uk">hashem.koohy@rdm.ox.ac.uk</email></corresp><fn fn-type="COI-statement"><p>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>25</day><month>2</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>9</volume><elocation-id>145</elocation-id><history><date date-type="accepted"><day>20</day><month>2</month><year>2020</year></date></history><permissions><copyright-statement>Copyright: © 2020 Hyun-Jung Lee C and Koohy H</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="f1000research-9-24839.pdf"></self-uri><abstract><p><bold>Background:</bold> The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.</p><p><bold>Methods:</bold> The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.</p><p><bold>Results:</bold> We report
<italic>in silico</italic> identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a
<italic>de novo</italic> search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.</p><p><bold>Conclusions:</bold> Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</p></abstract><kwd-group kwd-group-type="author"><kwd>Coronavirus</kwd><kwd>adaptive immunity</kwd><kwd>immunogenicity</kwd><kwd>T cell cross-reactivity</kwd><kwd>vaccine development</kwd></kwd-group><funding-group><award-group id="fund-1" xlink:href="http://dx.doi.org/10.13039/501100000265"><funding-source>Medical Research Council</funding-source></award-group><funding-statement>This study was funded by the Medical Research Council Human Immunology Unit. </funding-statement></funding-group></article-meta></front><sub-article id="report60502" article-type="peer-review"><front-stub><article-id pub-id-type="doi">10.5256/f1000research.24839.r60502</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>De Palma</surname><given-names>Raffaele</given-names></name><xref ref-type="aff" rid="r60502a1">1</xref><xref ref-type="aff" rid="r60502a2">2</xref><role>Referee</role></contrib><aff id="r60502a1"><label>1</label>DIMI, Department of Internal Medicine, University of Genova, Genova, Italy</aff><aff id="r60502a2"><label>2</label>IBBC (Istituto di Biochimica e Biologia Cellulare), CNR-Napoli, Naples, Italy</aff></contrib-group><author-notes><fn fn-type="COI-statement"><p><bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>31</day><month>3</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 De Palma R</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type="peer-reviewed-article" id="d35e2494" ext-link-type="doi" xlink:href="10.12688/f1000research.22507.1">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>The Authors use an
<italic>in silico</italic> approach to identify antigenic peptides derived from 2019-nCoV. First, they screened ten open reading frame of 2019-nCoV sequence used the IEDB database, finding a series of peptides potentially immunogenic. The first piece of data relies on the identification of 28 peptides that were exactly matching SARS CoV peptides. Moreover, using combinatory approaches, modelling HLA and TCR binding, they identified 13 peptides potentially able to bind a given TCR in HLA A2 restriction fashion, and cutting a list of peptides able to bind several HLA alleles and characterizing several peptides that may be de novo candidates or crossreactive peptides to be used either to study immune response to 2019-nCoV or to set a vaccine.</p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article><sub-article id="report60504" article-type="peer-review"><front-stub><article-id pub-id-type="doi">10.5256/f1000research.24839.r60504</article-id><title-group><article-title>Reviewer response for version 1</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wilkinson</surname><given-names>Katalin</given-names></name><xref ref-type="aff" rid="r60504a1">1</xref><xref ref-type="aff" rid="r60504a2">2</xref><role>Referee</role><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9796-2040</contrib-id></contrib><aff id="r60504a1"><label>1</label>Tuberculosis Laboratory, The Francis Crick Institute, London, UK</aff><aff id="r60504a2"><label>2</label>Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa</aff></contrib-group><author-notes><fn fn-type="COI-statement"><p><bold>Competing interests: </bold>No competing interests were disclosed.</p></fn></author-notes><pub-date pub-type="epub"><day>5</day><month>3</month><year>2020</year></pub-date><permissions><copyright-statement>Copyright: © 2020 Wilkinson K</copyright-statement><copyright-year>2020</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><related-article related-article-type="peer-reviewed-article" id="d35e2563" ext-link-type="doi" xlink:href="10.12688/f1000research.22507.1">Version 1</related-article><custom-meta-group><custom-meta><meta-name>recommendation</meta-name><meta-value>approve</meta-value></custom-meta></custom-meta-group></front-stub><body><p>This is an important manuscript identifying potential vaccine targets for 2019-nCoV, using computational prediction. In the absence of patient samples, such approaches are instrumental to guide towards quick and efficient identification of vaccine candidates. The authors used 10 open reading frame sequences of 2019-nCoV deposited at NCBI and conducted sequence alignment against immunogenic peptides deposited in the Immune Epitope Database and Analysis Resource (IEDB) database. They identified 28 peptides with sequences matching exactly to severe acute respiratory syndrome-related coronavirus (SARS CoV), that have previously been characterised as immunogenic by T cell assays. These findings are very promising and have the added benefit of potentially developing a vaccine against both SARS and COVID-19.</p><p> Additional peptides were identified to most likely bind common Chinese and European HLA alleles and have high immunogenicity potential. The authors provide a shortlist of peptides as potential vaccine candidates. While this manuscript presents a good model for identifying such targets, a comment should be included in the discussion about the necessity of expanding the analysis to include wider HLA allele types, considering that the virus is likely to spread worldwide.</p><p> Minor comment: Please explain the methods and analysis in greater details, including the following terms:
<list list-type="order"><list-item><p>‘Towards target peptide length > 3’.</p></list-item><list-item><p>‘Normalized alignment scores’ (and their scale, such as the significance of 4 for the data presented in Figure 1).</p></list-item><list-item><p>The importance of leucine (L) and valine (V) in anchor positions for MHC binding (for data presented in Table 5).</p></list-item></list></p><p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p></body></sub-article></pmc><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Oxfordshire</li></region><settlement><li>Oxford</li></settlement><orgName><li>Université d'Oxford</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Hyun Jung Lee, Chloe" sort="Hyun Jung Lee, Chloe" uniqKey="Hyun Jung Lee C" first="Chloe" last="Hyun-Jung Lee">Chloe Hyun-Jung Lee</name></region><name sortKey="Koohy, Hashem" sort="Koohy, Hashem" uniqKey="Koohy H" first="Hashem" last="Koohy">Hashem Koohy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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