Chu Qinyu, He Xinheng, Tan Xinyi, Gu Zhiyong, Luo Yin, Huang Zifu, Zheng Mingyue, Cheng Xi
School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study Hangzhou 330106 China
Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine Shanghai 200025 China
Chem Sci. 2025 May 27;16(23):10264-10272. doi: 10.1039/d5sc02262b. eCollection 2025 Jun 11.
Carbohydrate-protein interactions underlie key physiological and pathological processes, yet identification of glycan-binding sites remains challenging due to the complexity of glycans and a lack of dedicated computational tools. We present GlycanInsight, a deep learning-based open platform that predicts carbohydrate-binding pockets on protein structures. On the benchmark dataset of experimental structures, GlycanInsight achieves a high Matthews correlation coefficient (MCC) of 0.63, outperforming existing tools, and maintains robust performance on AlphaFold2-predicted structures (MCC = 0.53). GlycanInsight clusters predicted residues into three-dimensional carbohydrate-binding pockets for detailed structural inspection, quantitatively analyzes pocket characteristics, searches for other proteins with similar pockets, and suggests putative binding ligands for the predicted pockets. By integrating precise prediction with automated structural annotation and ligand retrieval, GlycanInsight facilitates mechanistic studies and rational design of glycan-targeted therapeutics. The platform is freely accessible at https://www.glycaninsight.cn/.
碳水化合物 - 蛋白质相互作用是关键生理和病理过程的基础，但由于聚糖的复杂性和缺乏专用计算工具，聚糖结合位点的识别仍然具有挑战性。我们展示了GlycanInsight，这是一个基于深度学习的开放平台，可预测蛋白质结构上的碳水化合物结合口袋。在实验结构的基准数据集上，GlycanInsight实现了0.63的高马修斯相关系数（MCC），优于现有工具，并在AlphaFold2预测的结构上保持稳健性能（MCC = 0.53）。GlycanInsight将预测的残基聚类到三维碳水化合物结合口袋中进行详细的结构检查，定量分析口袋特征，搜索具有相似口袋的其他蛋白质，并为预测的口袋建议推定的结合配体。通过将精确预测与自动结构注释和配体检索相结合，GlycanInsight促进了聚糖靶向治疗的机制研究和合理设计。该平台可在https://www.glycaninsight.cn/免费访问。
