GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome
GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome
复制标题
GlycoMine：一种基于机器学习的方法，用于预测人类蛋白质组中的 N、C 和 O 连接糖基化
DOI：
10.1093/bioinformatics/btu852
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发表时间：
2015-05-01
期刊：
影响因子：
5.8
通讯作者：
Song, Jiangning
中科院分区：
文献类型：
--
作者：
Li, Fuyi;Li, Chen;Song, Jiangning
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MOTIVATION
Glycosylation is a ubiquitous type of protein post-translational modification (PTM) in eukaryotic cells, which plays vital roles in various biological processes (BPs) such as cellular communication, ligand recognition and subcellular recognition. It is estimated that >50% of the entire human proteome is glycosylated. However, it is still a significant challenge to identify glycosylation sites, which requires expensive/laborious experimental research. Thus, bioinformatics approaches that can predict the glycan occupancy at specific sequons in protein sequences would be useful for understanding and utilizing this important PTM.
RESULTS
In this study, we present a novel bioinformatics tool called GlycoMine, which is a comprehensive tool for the systematic in silico identification of C-linked, N-linked, and O-linked glycosylation sites in the human proteome. GlycoMine was developed using the random forest algorithm and evaluated based on a well-prepared up-to-date benchmark dataset that encompasses all three types of glycosylation sites, which was curated from multiple public resources. Heterogeneous sequences and functional features were derived from various sources, and subjected to further two-step feature selection to characterize a condensed subset of optimal features that contributed most to the type-specific prediction of glycosylation sites. Five-fold cross-validation and independent tests show that this approach significantly improved the prediction performance compared with four existing prediction tools: NetNGlyc, NetOGlyc, EnsembleGly and GPP. We demonstrated that this tool could identify candidate glycosylation sites in case study proteins and applied it to identify many high-confidence glycosylation target proteins by screening the entire human proteome.
AVAILABILITY AND IMPLEMENTATION
The webserver, Java Applet, user instructions, datasets, and predicted glycosylation sites in the human proteome are freely available at http://www.structbioinfor.org/Lab/GlycoMine/.
CONTACT
Jiangning.Song@monash.edu or James.Whisstock@monash.edu or zhangyang@nwsuaf.edu.cn
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.