Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus
Identifieur interne : 000379 ( Pmc/Curation ); précédent : 000378; suivant : 000380Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus
Auteurs : Xiao-Li Qiang ; Peng Xu ; Gang Fang ; Wen-Bin Liu ; Zheng KouSource :
- Infectious Diseases of Poverty [ 2095-5162 ] ; 2020.
Abstract
Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning.
The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus.
The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual.
The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.
Url:
DOI: 10.1186/s40249-020-00649-8
PubMed: 32209118
PubMed Central: 7093988
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus</title><author><name sortKey="Qiang, Xiao Li" sort="Qiang, Xiao Li" uniqKey="Qiang X" first="Xiao-Li" last="Qiang">Xiao-Li Qiang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Xu, Peng" sort="Xu, Peng" uniqKey="Xu P" first="Peng" last="Xu">Peng Xu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Fang, Gang" sort="Fang, Gang" uniqKey="Fang G" first="Gang" last="Fang">Gang Fang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Liu, Wen Bin" sort="Liu, Wen Bin" uniqKey="Liu W" first="Wen-Bin" last="Liu">Wen-Bin Liu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Kou, Zheng" sort="Kou, Zheng" uniqKey="Kou Z" first="Zheng" last="Kou">Zheng Kou</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32209118</idno><idno type="pmc">7093988</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093988</idno><idno type="RBID">PMC:7093988</idno><idno type="doi">10.1186/s40249-020-00649-8</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000379</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000379</idno><idno type="wicri:Area/Pmc/Curation">000379</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000379</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus</title><author><name sortKey="Qiang, Xiao Li" sort="Qiang, Xiao Li" uniqKey="Qiang X" first="Xiao-Li" last="Qiang">Xiao-Li Qiang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Xu, Peng" sort="Xu, Peng" uniqKey="Xu P" first="Peng" last="Xu">Peng Xu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Fang, Gang" sort="Fang, Gang" uniqKey="Fang G" first="Gang" last="Fang">Gang Fang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Liu, Wen Bin" sort="Liu, Wen Bin" uniqKey="Liu W" first="Wen-Bin" last="Liu">Wen-Bin Liu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Kou, Zheng" sort="Kou, Zheng" uniqKey="Kou Z" first="Zheng" last="Kou">Zheng Kou</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Infectious Diseases of Poverty</title><idno type="ISSN">2095-5162</idno><idno type="eISSN">2049-9957</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p id="Par1">Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning.</p></sec><sec><title>Methods</title><p id="Par2">The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus.</p></sec><sec><title>Results</title><p id="Par3">The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual.</p></sec><sec><title>Conclusions</title><p id="Par4">The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Gorbalenya, A" uniqKey="Gorbalenya A">A Gorbalenya</name></author><author><name sortKey="Enjuanes, L" uniqKey="Enjuanes L">L Enjuanes</name></author><author><name sortKey="Ziebuhr, J" uniqKey="Ziebuhr J">J Ziebuhr</name></author><author><name sortKey="Snijder, E" uniqKey="Snijder E">E Snijder</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Corman, V" uniqKey="Corman V">V Corman</name></author><author><name sortKey="Muth, D" uniqKey="Muth D">D Muth</name></author><author><name sortKey="Niemeyer, D" uniqKey="Niemeyer D">D Niemeyer</name></author><author><name sortKey="Drosten, C" uniqKey="Drosten C">C Drosten</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cui, J" uniqKey="Cui J">J Cui</name></author><author><name sortKey="Li, F" uniqKey="Li F">F Li</name></author><author><name sortKey="Shi, Zl" uniqKey="Shi Z">ZL Shi</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Adams, M" uniqKey="Adams M">M Adams</name></author><author><name sortKey="Carstens, E" uniqKey="Carstens E">E Carstens</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Menachery, V" uniqKey="Menachery V">V Menachery</name></author><author><name sortKey="Yount, B" uniqKey="Yount B">B Yount</name></author><author><name sortKey="Debbink, K" uniqKey="Debbink K">K Debbink</name></author><author><name sortKey="Agnihothram, S" uniqKey="Agnihothram S">S Agnihothram</name></author><author><name sortKey="Gralinski, L" uniqKey="Gralinski L">L Gralinski</name></author><author><name sortKey="Plante, J" uniqKey="Plante J">J Plante</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Qiang, Xl" uniqKey="Qiang X">XL Qiang</name></author><author><name sortKey="Kou, Z" uniqKey="Kou Z">Z Kou</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Qiang, Xl" uniqKey="Qiang X">XL Qiang</name></author><author><name sortKey="Kou, Z" uniqKey="Kou Z">Z Kou</name></author><author><name sortKey="Fang, G" uniqKey="Fang G">G Fang</name></author><author><name sortKey="Wang, Y" uniqKey="Wang Y">Y Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heald Sargent, T" uniqKey="Heald Sargent T">T Heald-Sargent</name></author><author><name sortKey="Gallagher, T" uniqKey="Gallagher T">T Gallagher</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhao, Wm" uniqKey="Zhao W">WM Zhao</name></author><author><name sortKey="Song, Sh" uniqKey="Song S">SH Song</name></author><author><name sortKey="Chen, Ml" uniqKey="Chen M">ML Chen</name></author><author><name sortKey="Zou, D" uniqKey="Zou D">D Zou</name></author><author><name sortKey="Ma, Ln" uniqKey="Ma L">LN Ma</name></author><author><name sortKey="Ma, Yk" uniqKey="Ma Y">YK Ma</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, B" uniqKey="Liu B">B Liu</name></author><author><name sortKey="Liu, F" uniqKey="Liu F">F Liu</name></author><author><name sortKey="Wang, X" uniqKey="Wang X">X Wang</name></author><author><name sortKey="Chen, J" uniqKey="Chen J">J Chen</name></author><author><name sortKey="Fang, L" uniqKey="Fang L">L Fang</name></author><author><name sortKey="Chou, K" uniqKey="Chou K">K Chou</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Atchley, Wr" uniqKey="Atchley W">WR Atchley</name></author><author><name sortKey="Zhao, J" uniqKey="Zhao J">J Zhao</name></author><author><name sortKey="Fernandes, Ad" uniqKey="Fernandes A">AD 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Infect Dis Poverty</journal-id><journal-id journal-id-type="iso-abbrev">Infect Dis Poverty</journal-id><journal-title-group><journal-title>Infectious Diseases of Poverty</journal-title></journal-title-group><issn pub-type="ppub">2095-5162</issn><issn pub-type="epub">2049-9957</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32209118</article-id><article-id pub-id-type="pmc">7093988</article-id><article-id pub-id-type="publisher-id">649</article-id><article-id pub-id-type="doi">10.1186/s40249-020-00649-8</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Qiang</surname><given-names>Xiao-Li</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Xu</surname><given-names>Peng</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Fang</surname><given-names>Gang</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Wen-Bin</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Kou</surname><given-names>Zheng</given-names></name><address><email>kouzhengcn@foxmail.com</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.411863.9</institution-id><institution-id institution-id-type="ISNI">0000 0001 0067 3588</institution-id><institution>Institute of Computing Science and Technology, Guangzhou University,</institution></institution-wrap>Guangzhou, 510006 China</aff></contrib-group><pub-date pub-type="epub"><day>25</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="pmc-release"><day>25</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>9</volume><elocation-id>33</elocation-id><history><date date-type="received"><day>6</day><month>2</month><year>2020</year></date><date date-type="accepted"><day>16</day><month>3</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated in a credit line to the data.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p id="Par1">Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the infection risk of non-human-origin coronavirus for early warning.</p></sec><sec><title>Methods</title><p id="Par2">The spike protein sequences of 2666 coronaviruses were collected from 2019 Novel Coronavirus Resource (2019nCoVR) Database of China National Genomics Data Center on Jan 29, 2020. A total of 507 human-origin viruses were regarded as positive samples, whereas 2159 non-human-origin viruses were regarded as negative. To capture the key information of the spike protein, three feature encoding algorithms (amino acid composition, AAC; parallel correlation-based pseudo-amino-acid composition, PC-PseAAC and G-gap dipeptide composition, GGAP) were used to train 41 random forest models. The optimal feature with the best performance was identified by the multidimensional scaling method, which was used to explore the pattern of human coronavirus.</p></sec><sec><title>Results</title><p id="Par3">The 10-fold cross-validation results showed that well performance was achieved with the use of the GGAP (g = 3) feature. The predictive model achieved the maximum ACC of 98.18% coupled with the Matthews correlation coefficient (MCC) of 0.9638. Seven clusters for human coronaviruses (229E, NL63, OC43, HKU1, MERS-CoV, SARS-CoV, and SARS-CoV-2) were found. The cluster for SARS-CoV-2 was very close to that for SARS-CoV, which suggests that both of viruses have the same human receptor (angiotensin converting enzyme II). The big gap in the distance curve suggests that the origin of SARS-CoV-2 is not clear and further surveillance in the field should be made continuously. The smooth distance curve for SARS-CoV suggests that its close relatives still exist in nature and public health is challenged as usual.</p></sec><sec><title>Conclusions</title><p id="Par4">The optimal feature (GGAP, g = 3) performed well in terms of predicting infection risk and could be used to explore the evolutionary dynamic in a simple, fast and large-scale manner. The study may be beneficial for the surveillance of the genome mutation of coronavirus in the field.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Coronavirus</kwd><kwd>Cross-species infection</kwd><kwd>Spike protein</kwd><kwd>Machine learning</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001809</institution-id><institution>National Natural Science Foundation of China</institution></institution-wrap></funding-source><award-id>61972109</award-id><principal-award-recipient><name><surname>Kou</surname><given-names>Zheng</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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