Chen Xiang, Shi Shao-Ping, Suo Sheng-Bao, Xu Hao-Dong, Qiu Jian-Ding
Department of Chemistry, Nanchang University, Nanchang 330031, Department of Mathematics, Nanchang University, Nanchang 330031 and Department of Materials and Chemical Engineering, Pingxiang College, Pingxiang 337055, P.R. China.
Department of Chemistry, Nanchang University, Nanchang 330031, Department of Mathematics, Nanchang University, Nanchang 330031 and Department of Materials and Chemical Engineering, Pingxiang College, Pingxiang 337055, P.R. China Department of Chemistry, Nanchang University, Nanchang 330031, Department of Mathematics, Nanchang University, Nanchang 330031 and Department of Materials and Chemical Engineering, Pingxiang College, Pingxiang 337055, P.R. China.
Bioinformatics. 2015 Jan 15;31(2):194-200. doi: 10.1093/bioinformatics/btu598. Epub 2014 Sep 17.
Protein phosphorylation is the most common post-translational modification (PTM) regulating major cellular processes through highly dynamic and complex signaling pathways. Large-scale comparative phosphoproteomic studies have frequently been done on whole cells or organs by conventional bottom-up mass spectrometry approaches, i.e at the phosphopeptide level. Using this approach, there is no way to know from where the phosphopeptide signal originated. Also, as a consequence of the scale of these studies, important information on the localization of phosphorylation sites in subcellular compartments (SCs) is not surveyed.
Here, we present a first account of the emerging field of subcellular phosphoproteomics where a support vector machine (SVM) approach was combined with a novel algorithm of discrete wavelet transform (DWT) to facilitate the identification of compartment-specific phosphorylation sites and to unravel the intricate regulation of protein phosphorylation. Our data reveal that the subcellular phosphorylation distribution is compartment type dependent and that the phosphorylation displays site-specific sequence motifs that diverge between SCs.
The method and database both are available as a web server at: http://bioinfo.ncu.edu.cn/SubPhos.aspx.
Supplementary data are available at Bioinformatics online.
蛋白质磷酸化是最常见的翻译后修饰（PTM），通过高度动态和复杂的信号通路调节主要细胞过程。大规模比较磷酸化蛋白质组学研究通常通过传统的自下而上质谱方法在全细胞或器官上进行，即在磷酸肽水平上进行。使用这种方法，无法知道磷酸肽信号的来源。此外，由于这些研究的规模，亚细胞区室（SCs）中磷酸化位点定位的重要信息未被调查。
在这里，我们首次介绍了亚细胞磷酸化蛋白质组学这一新兴领域，其中支持向量机（SVM）方法与离散小波变换（DWT）的新算法相结合，以促进区室特异性磷酸化位点的鉴定，并揭示蛋白质磷酸化的复杂调控。我们的数据表明，亚细胞磷酸化分布取决于区室类型，并且磷酸化显示出在SCs之间不同的位点特异性序列基序。
该方法和数据库均可作为网络服务器在以下网址获得：http://bioinfo.ncu.edu.cn/SubPhos.aspx。
补充数据可在《生物信息学》在线获取。
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