Yom Aria, Chiang Austin, Lewis Nathan E
Department of Physics, University of California, San Diego. CA 92093, USA.
Department of Pediatrics, University of California, San Diego. CA 92093, USA.
bioRxiv. 2024 Mar 12:2023.06.03.543532. doi: 10.1101/2023.06.03.543532.
Glycans are complex oligosaccharides 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.
聚糖是参与多种疾病和生物过程的复杂寡糖。遗憾的是，目前用于确定聚糖组成和结构（聚糖测序）的方法既费力又需要高水平的专业知识。在此，我们评估基于凝集素结合指纹图谱对聚糖进行测序的可行性。通过在凝集素结合数据上训练玻尔兹曼模型，我们预测了测试集中88±7%的N-聚糖和87±13%的O-聚糖的近似结构。我们表明，我们的模型能够很好地推广到与药物相关的中国仓鼠卵巢（CHO）细胞聚糖的情况。我们还分析了多种凝集素的基序特异性，并确定了预测性最强和最弱的凝集素以及聚糖特征。这些结果有助于简化糖蛋白研究，对任何将凝集素用于糖生物学研究的人都有用处。
