UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines

Abstract

BackgroundThe conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process – E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes. This ubiquitin-conjugation process typically binds the last amino acid of ubiquitin (glycine 76) to a lysine residue of a target protein. The high-throughput of mass spectrometry-based proteomics has stimulated a large-scale identification of ubiquitin-conjugated peptides. Hence, a new web resource, UbiSite, was developed to identify ubiquitin-conjugation site on lysines based on large-scale proteome dataset.ResultsGiven a total of 37,647 ubiquitin-conjugated proteins, including 128026 ubiquitylated peptides, obtained from various resources, this study carries out a large-scale investigation on ubiquitin-conjugation sites based on sequenced and structural characteristics. A TwoSampleLogo reveals that a significant depletion of histidine (H), arginine (R) and cysteine (C) residues around ubiquitylation sites may impact the conjugation of ubiquitins in closed three-dimensional environments. Based on the large-scale ubiquitylation dataset, a motif discovery tool, MDDLogo, has been adopted to characterize the potential substrate motifs for ubiquitin conjugation. Not only are single features such as amino acid composition (AAC), positional weighted matrix (PWM), position-specific scoring matrix (PSSM) and solvent-accessible surface area (SASA) considered, but also the effectiveness of incorporating MDDLogo-identified substrate motifs into a two-layered prediction model is taken into account. Evaluation by five-fold cross-validation showed that PSSM is the best feature in discriminating between ubiquitylation and non-ubiquitylation sites, based on support vector machine (SVM). Additionally, the two-layered SVM model integrating MDDLogo-identified substrate motifs could obtain a promising accuracy and the Matthews Correlation Coefficient (MCC) at 81.06 % and 0.586, respectively. Furthermore, the independent testing showed that the two-layered SVM model could outperform other prediction tools, reaching at 85.10 % sensitivity, 69.69 % specificity, 73.69 % accuracy and the 0.483 of MCC value.ConclusionThe independent testing result indicated the effectiveness of incorporating MDDLogo-identified motifs into the prediction of ubiquitylation sites. In order to provide meaningful assistance to researchers interested in large-scale ubiquitinome data, the two-layered SVM model has been implemented onto a web-based system (UbiSite), which is freely available at http://csb.cse.yzu.edu.tw/UbiSite/. Two cases given in the UbiSite provide a demonstration of effective identification of ubiquitylation sites with reference to substrate motifs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0246-z) contains supplementary material, which is available to authorized users.

ZBP1/DAI ubiquitination and sensing of influenza vRNPs activate programmed cell death

“…RNA-binding properties of ZBP1 were predicted using protein-RNA–binding prediction analytical tools BindN, catRAPID, and aaRNA ( Wang and Brown, 2006 ; Agostini et al, 2013 ; Li et al, 2014 ). UbPred and UbiSite servers were used to predict potential ubiquitination sites in ZBP1 ( Radivojac et al, 2010 ; Huang et al, 2016 ). i-TASSER server was used to predict full-length ZBP1 structure ( Roy et al, 2010 ).…”

Functional Study of the Retrotransposon-Derived Human PEG10 Protease

“…Ubiquitination of human PEG10 was predicted by UbiSite (http://csb.cse.yzu.edu.tw/UbiSite/prediction. php) [48], BDM-PUB: Prediction of Ubiquitination sites with Bayesian Discriminant Method (http: //bdmpub.biocuckoo.org), and UbPred (predictor of protein ubiquitination sites) (http://www.ubpred. org/cgi-bin/ubpred/ubpred.cgi) [49] web servers, using default parameters.…”

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

“…Due to the difficulty of observing the conserved motifs from a large-scale sequence dataset, MDD has been utilized to cluster a group of aligned phosphorylated peptides into subgroups that show statistically significant motifs [ 20 ]. Previous studies [ 31 , 35 , 40 – 42 ] have demonstrated the effectiveness of the clustering of modified sequences into subgroups prior to the construction of predictive models. For this investigation, MDD was applied using public software, MDDLogo [ 31 ], to cluster all the sequence fragments of the positive training dataset.…”