Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses.
Identifieur interne : 001254 ( PubMed/Checkpoint ); précédent : 001253; suivant : 001255Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses.
Auteurs : Gerard Kian-Meng Goh ; A Keith Dunker ; Vladimir N. UverskySource :
- Journal of pathogens [ 2090-3065 ] ; 2012.
Abstract
Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior.
DOI: 10.1155/2012/738590
PubMed: 23097708
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Uversky</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:23097708</idno><idno type="pmid">23097708</idno><idno type="doi">10.1155/2012/738590</idno><idno type="wicri:Area/PubMed/Corpus">001292</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001292</idno><idno type="wicri:Area/PubMed/Curation">001292</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001292</idno><idno type="wicri:Area/PubMed/Checkpoint">001254</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001254</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses.</title><author><name sortKey="Goh, Gerard Kian Meng" sort="Goh, Gerard Kian Meng" uniqKey="Goh G" first="Gerard Kian-Meng" last="Goh">Gerard Kian-Meng Goh</name><affiliation><nlm:affiliation>Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA ; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228.</nlm:affiliation><wicri:noCountry code="subField">Singapore 119228</wicri:noCountry></affiliation></author><author><name sortKey="Dunker, A Keith" sort="Dunker, A Keith" uniqKey="Dunker A" first="A Keith" last="Dunker">A Keith Dunker</name></author><author><name sortKey="Uversky, Vladimir N" sort="Uversky, Vladimir N" uniqKey="Uversky V" first="Vladimir N" last="Uversky">Vladimir N. Uversky</name></author></analytic><series><title level="j">Journal of pathogens</title><idno type="eISSN">2090-3065</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">23097708</PMID><DateCompleted><Year>2012</Year><Month>10</Month><Day>26</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2090-3065</ISSN><JournalIssue CitedMedium="Internet"><Volume>2012</Volume><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>Journal of pathogens</Title><ISOAbbreviation>J Pathog</ISOAbbreviation></Journal><ArticleTitle>Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses.</ArticleTitle><Pagination><MedlinePgn>738590</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1155/2012/738590</ELocationID><Abstract><AbstractText>Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Goh</LastName><ForeName>Gerard Kian-Meng</ForeName><Initials>GK</Initials><AffiliationInfo><Affiliation>Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA ; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dunker</LastName><ForeName>A Keith</ForeName><Initials>AK</Initials></Author><Author ValidYN="Y"><LastName>Uversky</LastName><ForeName>Vladimir N</ForeName><Initials>VN</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal 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