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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Context-Specific Target Definition in Influenza A Virus Hemagglutinin – Glycan Receptor Interactions</title><author><name sortKey="Shriver, Zachary" sort="Shriver, Zachary" uniqKey="Shriver Z" first="Zachary" last="Shriver">Zachary Shriver</name></author><author><name sortKey="Raman, Rahul" sort="Raman, Rahul" uniqKey="Raman R" first="Rahul" last="Raman">Rahul Raman</name></author><author><name sortKey="Viswanathan, Karthik" sort="Viswanathan, Karthik" uniqKey="Viswanathan K" first="Karthik" last="Viswanathan">Karthik Viswanathan</name></author><author><name sortKey="Sasisekharan, Ram" sort="Sasisekharan, Ram" uniqKey="Sasisekharan R" first="Ram" last="Sasisekharan">Ram Sasisekharan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19716471</idno><idno type="pmc">3733240</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3733240</idno><idno type="RBID">PMC:3733240</idno><idno type="doi">10.1016/j.chembiol.2009.08.002</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000883</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000883</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Context-Specific Target Definition in Influenza A Virus Hemagglutinin – Glycan Receptor Interactions</title><author><name sortKey="Shriver, Zachary" sort="Shriver, Zachary" uniqKey="Shriver Z" first="Zachary" last="Shriver">Zachary Shriver</name></author><author><name sortKey="Raman, Rahul" sort="Raman, Rahul" uniqKey="Raman R" first="Rahul" last="Raman">Rahul Raman</name></author><author><name sortKey="Viswanathan, Karthik" sort="Viswanathan, Karthik" uniqKey="Viswanathan K" first="Karthik" last="Viswanathan">Karthik Viswanathan</name></author><author><name sortKey="Sasisekharan, Ram" sort="Sasisekharan, Ram" uniqKey="Sasisekharan R" first="Ram" last="Sasisekharan">Ram Sasisekharan</name></author></analytic><series><title level="j">Chemistry & biology</title><idno type="ISSN">1074-5521</idno><idno type="eISSN">1879-1301</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Summary</title><p id="P1">Protein-glycan interactions are important regulators for a variety of biological processes, ranging from immune recognition to anticoagulation. An important area of active research is directed towards understanding the role of host cell surface glycans as recognition sites for pathogen protein receptors. Recognition of cell surface glycans is a widely employed strategy for a variety of pathogens, including bacteria, parasites, and viruses. We present here a representative example of such an interaction: the binding of influenza A hemagglutinin (HA) to specific sialylated glycans on the cell surface of human upper airway epithelial cells, which initiates the infection cycle. We detail a generalizable strategy to understand the nature of protein-glycan interactions both structurally and biochemically, using HA as a model system. This strategy combines a <italic>top-down</italic> approach using available structural information to define important contacts between glycans and HA, with a <italic>bottom-up</italic> approach using data mining and informatics approaches to identify the common motifs which distinguish glycan binders from non-binders. By probing protein-glycan interactions simultaneously through <italic>top-down</italic> and <italic>bottom-up</italic> approaches we can scientifically validate a series of observations. This in turn provides additional confidence and surmounts known challenges in the study of protein-glycan interactions, such as accounting for multivalency, and thus truly defines concepts such as <italic>specificity</italic>, <italic>affinity,</italic> and <italic>avidity</italic>. With the advent of new technologies for glycomics—including glycan arrays, data mining solutions, and robust algorithms to model protein-glycan interactions—we anticipate that such combination approaches will become tractable for a wide variety of protein-glycan interactions.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9500160</journal-id><journal-id journal-id-type="pubmed-jr-id">20384</journal-id><journal-id journal-id-type="nlm-ta">Chem Biol</journal-id><journal-id journal-id-type="iso-abbrev">Chem. Biol.</journal-id><journal-title-group><journal-title>Chemistry & biology</journal-title></journal-title-group><issn pub-type="ppub">1074-5521</issn><issn pub-type="epub">1879-1301</issn></journal-meta><article-meta><article-id pub-id-type="pmid">19716471</article-id><article-id pub-id-type="pmc">3733240</article-id><article-id pub-id-type="doi">10.1016/j.chembiol.2009.08.002</article-id><article-id pub-id-type="manuscript">NIHMS239619</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Context-Specific Target Definition in Influenza A Virus Hemagglutinin – Glycan Receptor Interactions</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Shriver</surname><given-names>Zachary</given-names></name></contrib><contrib contrib-type="author"><name><surname>Raman</surname><given-names>Rahul</given-names></name></contrib><contrib contrib-type="author"><name><surname>Viswanathan</surname><given-names>Karthik</given-names></name></contrib><contrib contrib-type="author"><name><surname>Sasisekharan</surname><given-names>Ram</given-names></name><xref ref-type="corresp" rid="CR1">∥</xref></contrib><aff id="A1">Harvard-MIT Division of Health Sciences and Technology, Koch Institute for Integrative Cancer Research, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA 02139.</aff></contrib-group><author-notes><corresp id="CR1"><label>∥</label>Author to whom correspondence should be addressed: 77 Massachusetts Ave, Building 16-561, Cambridge, MA 02139. Phone: 617-258-9494; Fax: 617-258-9409; <email>rams@mit.edu</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>26</day><month>7</month><year>2013</year></pub-date><pub-date pub-type="ppub"><day>28</day><month>8</month><year>2009</year></pub-date><pub-date pub-type="pmc-release"><day>05</day><month>8</month><year>2013</year></pub-date><volume>16</volume><issue>8</issue><fpage>803</fpage><lpage>814</lpage><permissions><copyright-statement>© 2010 Published by Elsevier Ltd.</copyright-statement><copyright-year>2010</copyright-year></permissions><abstract><title>Summary</title><p id="P1">Protein-glycan interactions are important regulators for a variety of biological processes, ranging from immune recognition to anticoagulation. An important area of active research is directed towards understanding the role of host cell surface glycans as recognition sites for pathogen protein receptors. Recognition of cell surface glycans is a widely employed strategy for a variety of pathogens, including bacteria, parasites, and viruses. We present here a representative example of such an interaction: the binding of influenza A hemagglutinin (HA) to specific sialylated glycans on the cell surface of human upper airway epithelial cells, which initiates the infection cycle. We detail a generalizable strategy to understand the nature of protein-glycan interactions both structurally and biochemically, using HA as a model system. This strategy combines a <italic>top-down</italic> approach using available structural information to define important contacts between glycans and HA, with a <italic>bottom-up</italic> approach using data mining and informatics approaches to identify the common motifs which distinguish glycan binders from non-binders. By probing protein-glycan interactions simultaneously through <italic>top-down</italic> and <italic>bottom-up</italic> approaches we can scientifically validate a series of observations. This in turn provides additional confidence and surmounts known challenges in the study of protein-glycan interactions, such as accounting for multivalency, and thus truly defines concepts such as <italic>specificity</italic>, <italic>affinity,</italic> and <italic>avidity</italic>. With the advent of new technologies for glycomics—including glycan arrays, data mining solutions, and robust algorithms to model protein-glycan interactions—we anticipate that such combination approaches will become tractable for a wide variety of protein-glycan interactions.</p></abstract><funding-group><award-group><funding-source country="United States">National Institute of General Medical Sciences : NIGMS</funding-source><award-id>U54 GM062116 || GM</award-id></award-group><award-group><funding-source country="United States">National Institute of General Medical Sciences : NIGMS</funding-source><award-id>R01 GM057073 || GM</award-id></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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