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.
PLoS Comput Biol. 2018 Apr 20;14(4):e1006093. doi: 10.1371/journal.pcbi.1006093. eCollection 2018 Apr.
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.
越来越多的证据表明，聚糖不仅起到“盾牌”的作用，而且对 HIV 包膜 (Env) 糖蛋白的抗原性至关重要，是许多广泛中和抗体 (bNAb) 的关键抗原决定簇。虽然许多研究都集中在定义单个聚糖或邻近聚糖组在 bNAb 结合中的作用，但对于改变整体聚糖景观如何调节抗体进入和 Env 抗原性知之甚少。在这里，我们开发了一种系统糖生物学方法来反向工程 HIV 聚糖异质性的复杂性，以指导基于抗原性的从头糖蛋白设计。我们评估了针对 94 种具有明确聚糖位点占有率的重组 gp120 单体的 bNAb 结合。使用贝叶斯机器学习算法，确定了 bNAb 特异性聚糖足迹，并将其用于设计抗原，以选择性改变 bNAb 的抗原性作为概念验证。我们的方法提供了一种新的设计策略，通过改变聚糖地形来预测性地调节抗原性，从而使体液免疫反应集中在 HIV 的病毒脆弱部位。
