Abstract
Glycosylation is one of the most extensive post-translation modifications of proteins. Although lots of computational models have been developed to predict the glycosylation sites, none of them considered the tissue and cell specificity of glycosylation. Here, we built a two-step computational method GlycoCell to predict the cell-specific O-GalNAc glycosylation, the most complex type of O-glycosylation reported so far, in 12 human cell types. The first step predicted whether a site had the potential to be O-glycosylated. The model achieved an accuracy of 0.83. The second step predicted whether a potential glycosite would be O-glycosylated in the given cell type. For 12 cell types, a model was built for each cell type. The accuracies for these models ranged from 0.78 to 0.87. To facilitate the usage of GlycoCell for the public, a web server was built which is available at http://www.biomedcloud.com.cn/GlyoCell/main.htm. It could be useful for investigating the cell-specific O-glycosylation in human.
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Acknowledgements
This study was supported by the National Natural Science Foundation (31500126 and 31371338), the National Key Plan for Scientific Research and Development of China (2016YFD0500300 and 2016YFC1200200) and the International Scientific and Technological Cooperation project (2014DFB30010).
There are no conflicts of interest.
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Zou, Y., Li, K., Jiang, T., Peng, Y. (2017). Prediction of Cell Specific O-GalNAc Glycosylation in Human. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_23
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DOI: https://doi.org/10.1007/978-981-10-6388-6_23
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