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PLoS Comput Biol. 2018 Apr 20;14(4):e1006093. doi: 10.1371/journal.pcbi.1006093. eCollection 2018 Apr.

Exploiting glycan topography for computational design of Env glycoprotein antigenicity.

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Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, United States of America.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, The University of Georgia, Athens, Georgia, United States of America.
Institute for HIV Research, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, United States of America.
Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States of America.


Mounting 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.

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