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GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome

机译: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联糖基化

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摘要

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.
机译:动机:糖基化是真核细胞中普遍存在的蛋白质翻译后修饰(PTM)类型,在各种生物过程(BP)中发挥重要作用,例如细胞通讯,配体识别和亚细胞识别。据估计,整个人类蛋白质组中> 50%被糖基化。然而,鉴定糖基化位点仍然是一个巨大的挑战,这需要昂贵/费力的实验研究。因此,可以预测蛋白质序列中特定序列糖基占用的生物信息学方法将有助于理解和利用这一重要的PTM。

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