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Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ub iquitylation- S UMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation. [ABSTRACT FROM AUTHOR]

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.Contact: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Systematic dissection of the ubiquitylation proteome is emerging as an appealing but challenging research topic because of the significant roles ubiquitylation play not only in protein degradation but also in many other cellular functions. High-throughput experimental studies using mass spectrometry have identified many ubiquitylation sites, primarily from eukaryotes. However, the vast majority of ubiquitylation sites remain undiscovered, even in well-studied systems. Because mass spectrometry–based experimental approaches for identifying ubiquitylation events are costly, time-consuming and biased toward abundant proteins and proteotypic peptides, in silico prediction of ubiquitylation sites is a potentially useful alternative strategy for whole proteome annotation. Because of various limitations, current ubiquitylation site prediction tools were not well designed to comprehensively assess proteomes.Results: We present a novel tool known as UbiProber, specifically designed for large-scale predictions of both general and species-specific ubiquitylation sites. We collected proteomics data for ubiquitylation from multiple species from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates the information from key positions and key amino acid residues. Cross-validation tests reveal that UbiProber achieves some improvement over existing tools in predicting species-specific ubiquitylation sites. Moreover, independent tests show that UbiProber improves the areas under receiver operating characteristic curves by ∼15% by using the Combined model.Availability: The UbiProber server is freely available on the web at http://bioinfo.ncu.edu.cn/UbiProber.aspx. The software system of UbiProber can be downloaded at the same site.Contact: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]