Proteomic analysis and prediction of human phosphorylation sites in subcellular level reveal subcellular specificity.

Bioinformatics

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.

Published: January 2015

Motivation: 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.

Results: 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.

Availability And Implementation: The method and database both are available as a web server at: http://bioinfo.ncu.edu.cn/SubPhos.aspx.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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Source
http://dx.doi.org/10.1093/bioinformatics/btu598DOI Listing

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