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Updated: Jan 27, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and
Ghazaleh Taherzadeh1, Abdollah Dehzangi2, Maryam Golchin1
1School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.
Motivation:
Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.
Results:
The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features.
Availability And Implementation:
http://sparks-lab.org/server/SPRINT-Gly/.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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