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dc.contributor.authorZhao, Peng
dc.contributor.authorDraghi, Monia
dc.contributor.authorArevalo, Claudia
dc.contributor.authorKarsten, Christina B.
dc.contributor.authorSuscovich, Todd J.
dc.contributor.authorGunn, Bronwyn
dc.contributor.authorStreeck, Hendrik
dc.contributor.authorBrass, Abraham L.
dc.contributor.authorTiemeyer, Michael
dc.contributor.authorSeaman, Michael
dc.contributor.authorMascola, John R.
dc.contributor.authorWells, Lance
dc.contributor.authorAlter, Galit
dc.contributor.authorYu, Wen-Han
dc.contributor.authorLauffenburger, Douglas A
dc.date.accessioned2018-09-11T14:02:57Z
dc.date.available2018-09-11T14:02:57Z
dc.date.issued2018-04
dc.date.submitted2017-05
dc.identifier.issn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/1721.1/117701
dc.description.abstractMounting evidence suggests that glycans, rather than merely serving as a “shield”, contribute critically to antigenicity of the HIV envelope (Env) glycoprotein, representing critical antigenic determinants for many broadly neutralizing antibodies (bNAbs). While many studies have focused on defining the role of individual glycans or groups of proximal glycans in bNAb binding, little is known about the effects of changes in the overall glycan landscape in modulating antibody access and Env antigenicity. Here we developed a systems glycobiology approach to reverse engineer the complexity of HIV glycan heterogeneity to guide antigenicity-based de novo glycoprotein design. bNAb binding was assessed against a panel of 94 recombinant gp120 monomers exhibiting defined glycan site occupancies. Using a Bayesian machine learning algorithm, bNAb-specific glycan footprints were identified and used to design antigens that selectively alter bNAb antigenicity as a proof-of concept. Our approach provides a new design strategy to predictively modulate antigenicity via the alteration of glycan topography, thereby focusing the humoral immune response on sites of viral vulnerability for HIV.en_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1006093en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleExploiting glycan topography for computational design of Env glycoprotein antigenicityen_US
dc.typeArticleen_US
dc.identifier.citationYu, Wen-Han, Peng Zhao, Monia Draghi, Claudia Arevalo, Christina B. Karsten, Todd J. Suscovich, Bronwyn Gunn, et al. “Exploiting Glycan Topography for Computational Design of Env Glycoprotein Antigenicity.” Edited by Greg Tucker-Kellogg. PLOS Computational Biology 14, 4 (April 2018): e1006093 © 2018 Public Library of Science (PLoS)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.mitauthorYu, Wen-Han
dc.contributor.mitauthorLauffenburger, Douglas A
dc.relation.journalPLOS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-09-07T18:31:36Z
dspace.orderedauthorsYu, Wen-Han; Zhao, Peng; Draghi, Monia; Arevalo, Claudia; Karsten, Christina B.; Suscovich, Todd J.; Gunn, Bronwyn; Streeck, Hendrik; Brass, Abraham L.; Tiemeyer, Michael; Seaman, Michael; Mascola, John R.; Wells, Lance; Lauffenburger, Douglas A.; Alter, Galiten_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-0050-989X
mit.licensePUBLISHER_CCen_US


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