Boltzmann Model Predicts Glycan Structures from Lectin Binding

Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.聚糖（Glycans）是一类复杂的寡糖，参与诸多疾病进程与生物学过程。遗憾的是，当前用于测定聚糖组成与结构（即聚糖测序）的方法繁琐费力，且需要极高的专业技术水平。本研究评估了基于凝集素（lectin）结合指纹图谱开展聚糖测序的可行性。通过在凝集素结合数据集上训练玻尔兹曼模型（Boltzmann model），我们在测试集中实现了对88±7%的N-聚糖（N-glycans）与87±13%的O-聚糖（O-glycans）的近似结构预测。研究表明，该模型可良好泛化至与制药行业高度相关的中国仓鼠卵巢（Chinese hamster ovary, CHO）细胞聚糖场景。我们还分析了多种凝集素的基序特异性，筛选出了预测能力最强与最弱的凝集素及聚糖特征。本研究成果可助力糖蛋白研究流程的简化，并可为所有将凝集素应用于糖生物学（glycobiology）领域的研究者提供参考。