Structures used for this work.

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or “trees”. Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

碳水化合物与糖蛋白可调控诸多关键生物学功能。然而，糖类聚合物的实验结构测定向来极具挑战性。计算方法可助力糖类结构预测、结构解析与设计工作。本研究开发了一款糖链建模算法GlycanTreeModeler，该算法借助源自蛋白质数据库（Protein Data Bank, PDB）与量子力学（quantum mechanics, QM）计算数据的常见糖链构象自适应核密度估计（adaptive kernel density estimates, KDE），逐层构建糖链结构。本研究针对不同长度的糖链结构（即“糖树”）构建测试集，对GlycanTreeModeler进行了基准测试。无论是从头建模（de novo modeling）还是基于实验密度引导的结构构建，GlycanTreeModeler预测的糖链结构均与天然结构保持极高吻合度。本研究利用该工具将从头设计的糖链整合至蛋白质纳米颗粒疫苗中，以遮蔽支架蛋白的抗体识别区域，并通过实验验证了该遮蔽效果。本研究可为糖蛋白模型预测、糖链遮蔽工作提供参考，并进一步助力实验结构解析与精修领域的计算方法发展。