Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.
Identifieur interne : 000133 ( PubMed/Corpus ); précédent : 000132; suivant : 000134Using 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 [ 2049-9957 ] ; 2020.
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.
DOI: 10.1186/s40249-020-00649-8
PubMed: 32209118
Links to Exploration step
pubmed:32209118Le document en format XML
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China.</nlm:affiliation></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:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</nlm:affiliation></affiliation></author><author><name sortKey="Kou, Zheng" sort="Kou, Zheng" uniqKey="Kou Z" first="Zheng" last="Kou">Zheng Kou</name><affiliation><nlm:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China. kouzhengcn@foxmail.com.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32209118</idno><idno type="pmid">32209118</idno><idno type="doi">10.1186/s40249-020-00649-8</idno><idno type="wicri:Area/PubMed/Corpus">000133</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000133</idno></publicationStmt><sourceDesc><biblStruct><analytic><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:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</nlm:affiliation></affiliation></author><author><name sortKey="Xu, Peng" sort="Xu, Peng" uniqKey="Xu P" first="Peng" last="Xu">Peng Xu</name><affiliation><nlm:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fang, Gang" sort="Fang, Gang" uniqKey="Fang G" first="Gang" last="Fang">Gang Fang</name><affiliation><nlm:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</nlm:affiliation></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:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</nlm:affiliation></affiliation></author><author><name sortKey="Kou, Zheng" sort="Kou, Zheng" uniqKey="Kou Z" first="Zheng" last="Kou">Zheng Kou</name><affiliation><nlm:affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China. kouzhengcn@foxmail.com.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Infectious diseases of poverty</title><idno type="eISSN">2049-9957</idno><imprint><date when="2020" type="published">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="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="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="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="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></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32209118</PMID><DateCompleted><Year>2020</Year><Month>04</Month><Day>02</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>09</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2049-9957</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>Mar</Month><Day>25</Day></PubDate></JournalIssue><Title>Infectious diseases of poverty</Title><ISOAbbreviation>Infect Dis Poverty</ISOAbbreviation></Journal><ArticleTitle>Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.</ArticleTitle><Pagination><MedlinePgn>33</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s40249-020-00649-8</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">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.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">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.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qiang</LastName><ForeName>Xiao-Li</ForeName><Initials>XL</Initials><AffiliationInfo><Affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Peng</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fang</LastName><ForeName>Gang</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Wen-Bin</ForeName><Initials>WB</Initials><AffiliationInfo><Affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kou</LastName><ForeName>Zheng</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China. kouzhengcn@foxmail.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>61972109</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate 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