UMMS Affiliation

Department of Microbiology and Physiological Systems

Publication Date

4-20-2018

Document Type

Article

Disciplines

Computational Biology | Hemic and Immune Systems | Immunology and Infectious Disease | Molecular Biology | Systems Biology

Abstract

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.

Rights and Permissions

Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

DOI of Published Version

10.1371/journal.pcbi.1006093

Source

PLoS Comput Biol. 2018 Apr 20;14(4):e1006093. doi: 10.1371/journal.pcbi.1006093. eCollection 2018 Apr. Link to article on publisher's site

Journal/Book/Conference Title

PLoS computational biology

Related Resources

Link to Article in PubMed

PubMed ID

29677181

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons 1.0 Public Domain Dedication.

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