Identification, analysis, and prediction of protein ubiquitination sites - PubMed (original) (raw)

Identification, analysis, and prediction of protein ubiquitination sites

Predrag Radivojac et al. Proteins. 2010.

Abstract

Ubiquitination plays an important role in many cellular processes and is implicated in many diseases. Experimental identification of ubiquitination sites is challenging due to rapid turnover of ubiquitinated proteins and the large size of the ubiquitin modifier. We identified 141 new ubiquitination sites using a combination of liquid chromatography, mass spectrometry, and mutant yeast strains. Investigation of the sequence biases and structural preferences around known ubiquitination sites indicated that their properties were similar to those of intrinsically disordered protein regions. Using a combined set of new and previously known ubiquitination sites, we developed a random forest predictor of ubiquitination sites, UbPred. The class-balanced accuracy of UbPred reached 72%, with the area under the ROC curve at 80%. The application of UbPred showed that high confidence Rsp5 ubiquitin ligase substrates and proteins with very short half-lives were significantly enriched in the number of predicted ubiquitination sites. Proteome-wide prediction of ubiquitination sites in Saccharomyces cerevisiae indicated that highly ubiquitinated substrates were prevalent among transcription/enzyme regulators and proteins involved in cell cycle control. In the human proteome, cytoskeletal, cell cycle, regulatory, and cancer-associated proteins display higher extent of ubiquitination than proteins from other functional categories. We show that gain and loss of predicted ubiquitination sites may likely represent a molecular mechanism behind a number of disease-associatedmutations. UbPred is available at http://www.ubpred.org.

(c) 2009 Wiley-Liss, Inc.

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Figures

Figure 1

Figure 1

A Two Sample Logo of the compositional biases around Ub sites compared to the non-Ub sites. Only amino acid residues significantly enriched and depleted (P < 0.05; _t_-test) from the positive dataset are shown.

Figure 2

Figure 2

Relative amino acid compositions of three studied datasets. Amino acid compositions are shown relative to the composition of ordered proteins from O_PDB_S25 database. Amino acids are arranged from left to right in order of increasing flexibility as defined by Vihinen et al. The error bars represent 95% confidence intervals.

Figure 3

Figure 3

Receiver operating characteristic (ROC) curve for the UbPred predictor of ubiquitination sites (solid line) vs. the performance of the random model (dotted line). The area under the curve (AUC) was estimated to be 79.6%.

Figure 4

Figure 4

GO annotations for the highly ubiquitinated proteins from S. cerevisiae proteome (colored bars) with occurrence of >5% (Bonferroni corrected) as compared to the entire yeast proteome (black bars). Top 20 (whenever available) GO Slim terms are shown. A. Molecular function; B. Biological process; C. Cellular component. The proteins are arranged in order of the decreasing fraction of proteins with a specific GO annotation present in the predicted highly ubiquitinated dataset. P-values were calculated using the hypergeometric distribution and corrected for multiple hypothesis testing. *** -P<0.0001; ** - P<0.001; * - P<0.05.

Figure 5

Figure 5

Frequencies of highly ubiquitinated proteins in eleven functional categories from Swiss-Prot as compared to the entire human proteome. P-values were calculated using the Wilcoxon test.

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