期刊
SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/srep34595
关键词
-
资金
- National Health and Medical Research Council of Australia (NHMRC) [1092262]
- National Natural Science Foundation of China [61202167, 61303169]
- Hundred Talents Program of the Chinese Academy of Sciences (CAS)
- Discovery Outstanding Research Award (DORA) of the Australian Research Council (ARC)
- Hundred Talents Program of CAS
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据