Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus
Identifieur interne : 000029 ( Main/Exploration ); précédent : 000028; suivant : 000030Using 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.
Descripteurs français
- KwdFr :
- Acides aminés (génétique), Algorithmes, Animaux, Chine, Coronavirus (génétique), Coronavirus (immunologie), Coronavirus (isolement et purification), Endopeptidases (génétique), Endopeptidases (métabolisme), Glycoprotéine de spicule des coronavirus (immunologie), Génome (génétique), Génome viral (génétique), Humains, Infections à coronavirus (génétique), Infections à coronavirus (transmission), Infections à coronavirus (virologie), Pandémies (), Peptidyl-Dipeptidase A (génétique), Peptidyl-Dipeptidase A (métabolisme), Phylogénie, Pneumopathie virale (génétique), Pneumopathie virale (transmission), Pneumopathie virale (virologie), Récepteurs viraux (génétique), Récepteurs viraux (métabolisme), Réservoirs d'agents pathogènes (virologie), Évaluation des risques.
- MESH :
- génétique : Acides aminés, Coronavirus, Endopeptidases, Génome, Génome viral, Infections à coronavirus, Peptidyl-Dipeptidase A, Pneumopathie virale, Récepteurs viraux.
- immunologie : Coronavirus, Glycoprotéine de spicule des coronavirus.
- isolement et purification : Coronavirus.
- métabolisme : Endopeptidases, Peptidyl-Dipeptidase A, Récepteurs viraux.
- virologie : Infections à coronavirus, Pneumopathie virale, Réservoirs d'agents pathogènes.
- Algorithmes, Animaux, Chine, Humains, Infections à coronavirus, Pandémies, Phylogénie, Pneumopathie virale, Évaluation des risques.
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Algorithms, Amino Acids (genetics), Animals, Betacoronavirus (genetics), Betacoronavirus (immunology), China, Chlorocebus aethiops, Coronavirus (genetics), Coronavirus (immunology), Coronavirus (isolation & purification), Coronavirus Infections (genetics), Coronavirus Infections (transmission), Coronavirus Infections (virology), Disease Reservoirs (virology), Endopeptidases (genetics), Endopeptidases (metabolism), Genome (genetics), Genome, Viral (genetics), Humans, Pandemics (prevention & control), Peptidyl-Dipeptidase A (genetics), Peptidyl-Dipeptidase A (metabolism), Phylogeny, Pneumonia, Viral (genetics), Pneumonia, Viral (transmission), Pneumonia, Viral (virology), Receptors, Virus (genetics), Receptors, Virus (metabolism), Risk Assessment, Spike Glycoprotein, Coronavirus (immunology).
- MESH :
- chemical , genetics : Amino Acids, Endopeptidases, Peptidyl-Dipeptidase A, Receptors, Virus.
- chemical , immunology : Spike Glycoprotein, Coronavirus.
- chemical , metabolism : Endopeptidases, Peptidyl-Dipeptidase A, Receptors, Virus.
- geographic : China.
- genetics : Betacoronavirus, Coronavirus, Coronavirus Infections, Genome, Genome, Viral, Pneumonia, Viral.
- immunology : Betacoronavirus, Coronavirus.
- isolation & purification : Coronavirus.
- prevention & control : Pandemics.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- virology : Coronavirus Infections, Disease Reservoirs, Pneumonia, Viral.
- Algorithms, Animals, Chlorocebus aethiops, Humans, Phylogeny, Risk Assessment.
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
Affiliations:
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Le document en format XML
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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><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Amino Acids (genetics)</term><term>Animals</term><term>Betacoronavirus (genetics)</term><term>Betacoronavirus (immunology)</term><term>China</term><term>Chlorocebus aethiops</term><term>Coronavirus (genetics)</term><term>Coronavirus (immunology)</term><term>Coronavirus (isolation & purification)</term><term>Coronavirus Infections (genetics)</term><term>Coronavirus Infections (transmission)</term><term>Coronavirus Infections (virology)</term><term>Disease Reservoirs (virology)</term><term>Endopeptidases (genetics)</term><term>Endopeptidases (metabolism)</term><term>Genome (genetics)</term><term>Genome, Viral (genetics)</term><term>Humans</term><term>Pandemics (prevention & control)</term><term>Peptidyl-Dipeptidase A (genetics)</term><term>Peptidyl-Dipeptidase A (metabolism)</term><term>Phylogeny</term><term>Pneumonia, Viral (genetics)</term><term>Pneumonia, Viral (transmission)</term><term>Pneumonia, Viral (virology)</term><term>Receptors, Virus (genetics)</term><term>Receptors, Virus (metabolism)</term><term>Risk Assessment</term><term>Spike Glycoprotein, Coronavirus (immunology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Acides aminés (génétique)</term><term>Algorithmes</term><term>Animaux</term><term>Chine</term><term>Coronavirus (génétique)</term><term>Coronavirus (immunologie)</term><term>Coronavirus (isolement et purification)</term><term>Endopeptidases (génétique)</term><term>Endopeptidases (métabolisme)</term><term>Glycoprotéine de spicule des coronavirus (immunologie)</term><term>Génome (génétique)</term><term>Génome viral (génétique)</term><term>Humains</term><term>Infections à coronavirus (génétique)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (virologie)</term><term>Pandémies ()</term><term>Peptidyl-Dipeptidase A (génétique)</term><term>Peptidyl-Dipeptidase A (métabolisme)</term><term>Phylogénie</term><term>Pneumopathie virale (génétique)</term><term>Pneumopathie virale (transmission)</term><term>Pneumopathie virale (virologie)</term><term>Récepteurs viraux (génétique)</term><term>Récepteurs viraux (métabolisme)</term><term>Réservoirs d'agents pathogènes (virologie)</term><term>Évaluation des risques</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Amino Acids</term><term>Endopeptidases</term><term>Peptidyl-Dipeptidase A</term><term>Receptors, Virus</term></keywords><keywords scheme="MESH" type="chemical" qualifier="immunology" xml:lang="en"><term>Spike Glycoprotein, Coronavirus</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Endopeptidases</term><term>Peptidyl-Dipeptidase A</term><term>Receptors, Virus</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Betacoronavirus</term><term>Coronavirus</term><term>Coronavirus Infections</term><term>Genome</term><term>Genome, Viral</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Acides aminés</term><term>Coronavirus</term><term>Endopeptidases</term><term>Génome</term><term>Génome viral</term><term>Infections à coronavirus</term><term>Peptidyl-Dipeptidase A</term><term>Pneumopathie virale</term><term>Récepteurs viraux</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Coronavirus</term><term>Glycoprotéine de spicule des coronavirus</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Betacoronavirus</term><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Coronavirus</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Endopeptidases</term><term>Peptidyl-Dipeptidase A</term><term>Récepteurs viraux</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Pandemics</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>Réservoirs d'agents pathogènes</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term><term>Disease Reservoirs</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Chlorocebus aethiops</term><term>Humans</term><term>Phylogeny</term><term>Risk Assessment</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Animaux</term><term>Chine</term><term>Humains</term><term>Infections à coronavirus</term><term>Pandémies</term><term>Phylogénie</term><term>Pneumopathie virale</term><term>Évaluation des risques</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term></keywords></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 Fernandes</name></author><author><name sortKey="Druke, T" uniqKey="Druke T">T Drüke</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liaw, A" uniqKey="Liaw A">A Liaw</name></author><author><name sortKey="Wiener, M" uniqKey="Wiener M">M Wiener</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sing, T" uniqKey="Sing T">T Sing</name></author><author><name sortKey="Sander, O" uniqKey="Sander O">O Sander</name></author><author><name sortKey="Beerenwinkel, N" uniqKey="Beerenwinkel N">N Beerenwinkel</name></author><author><name sortKey="Lengauer, T" uniqKey="Lengauer T">T Lengauer</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Holshue, Ml" uniqKey="Holshue M">ML Holshue</name></author><author><name sortKey="Debolt, C" uniqKey="Debolt C">C DeBolt</name></author><author><name sortKey="Lindquist, S" uniqKey="Lindquist S">S Lindquist</name></author><author><name sortKey="Lofy, Kh" uniqKey="Lofy K">KH Lofy</name></author><author><name sortKey="Wiesman, J" uniqKey="Wiesman J">J Wiesman</name></author><author><name sortKey="Bruce, H" uniqKey="Bruce H">H Bruce</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list></list><tree><noCountry><name sortKey="Fang, Gang" sort="Fang, Gang" uniqKey="Fang G" first="Gang" last="Fang">Gang Fang</name><name sortKey="Kou, Zheng" sort="Kou, Zheng" uniqKey="Kou Z" first="Zheng" last="Kou">Zheng Kou</name><name sortKey="Liu, Wen Bin" sort="Liu, Wen Bin" uniqKey="Liu W" first="Wen-Bin" last="Liu">Wen-Bin Liu</name><name sortKey="Qiang, Xiao Li" sort="Qiang, Xiao Li" uniqKey="Qiang X" first="Xiao-Li" last="Qiang">Xiao-Li Qiang</name><name sortKey="Xu, Peng" sort="Xu, Peng" uniqKey="Xu P" first="Peng" last="Xu">Peng Xu</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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