Glycosylation plays an important role in cell-cell adhesion, ligand-binding and subcellular recognition. Current approaches for predicting protein glycosylation are primarily based on sequence-derived features, while little work has been done to systematically assess the importance of structural features to glycosylation prediction. Here, we propose a novel bioinformatics method called GlycoMine struct (http://glycomine. erc. monash. edu/Lab/GlycoMine_Struct/) for improved prediction of human N-and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy. Experiments indicated that GlycoMine struct outperformed NGlycPred, the only predictor that incorporated both sequence and structure features, achieving AUC values of 0.941 and 0.922 for N-and O-linked glycosylation, respectively, on an independent test dataset. We applied GlycoMine struct to screen the human structural proteome and obtained high-confidence predictions for N-and O-linked glycosylation sites. GlycoMine struct can be used as a powerful tool to expedite the discovery of glycosylation events and substrates to facilitate hypothesis-driven experimental studies.