O-GlyThr: Prediction of human O-linked threonine glycosites using multi-feature fusion

O-linked glycosylation is one of the most complex post-translational modifications (PTM) of human proteins modulating various cellular metabolic and signaling pathways. Unlike N-glycosylation, the O-glycosylation has non-specific sequence features and unstable glycan core structure, which makes identification of O-glycosites more challenging either by experimental or computational methods. Biochemical experiments to identify Oglycosites in batches are technically and economically demanding. Therefore, development of computationbased methods is greatly warranted. This study constructed a prediction model based on feature fusion for Oglycosites linked to the threonine residues in Homo sapiens. In the training model, we collected and sorted out high-quality human protein data with O-linked threonine glycosites. Seven feature coding methods were fused to represent the sample sequence. By comparison of different algorithms, random forest was selected as the final classifier to construct the classification model. Through 5-fold cross-validation, the proposed model, namely OGlyThr, performed satisfactorily on both training set (AUC: 0.9308) and independent validation dataset (AUC: 0.9323). Compared with previously published predictors, O-GlyThr achieved the highest ACC of 0.8475 on the independent test dataset. These results demonstrated the high competency of our predictor in identifying Oglycosites on threonine residues. Furthermore, a user-friendly webserver named O-GlyThr (http://cbcb.cdutcm. edu.cn/O-GlyThr/) was developed to assist glycobiologists in the research associated with glycosylation structure and function.

Automated summary

This study constructed a prediction model based on feature fusion to predict O-glycosites linked to threonine residues in human proteins. Random forest was selected as the final classifier and achieved high accuracy on both training and validation datasets. The developed webserver, O-GlyThr, assists glycobiologists in researching glycosylation structure and function.