Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites

Abstract

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

Post-translational regulation of the cleaved fragment of Par-4 in ovarian and endometrial cancer cells

“…Phosphosite plus supports this prediction with six different manuscripts which predicted the K333 site using proteomic discovery-mode mass spectrometry, thus strengthening the likelihood of a true ubiquitination site [41, 42]. In parallel, we used three additional bioinformatics tools (Ubiprober, Ubpred, BDM-PUB) and identified 13 candidate ubiquitination sites, including the K333 site previously predicted by discovery-mode mass spectrometry (Table 1) [43–45]. By looking at the position of each predicted site, we found that K185 and K188 are located within the SAC domain while K305, K325, K329 and K333 are located within the leucine zipper domain (Table 1) (Figure 4C).…”

Protein post-translational modifications: In silico prediction tools and molecular modeling

“…On the basis of this observation UbPred was developed [262] (Table 1): a ubiquitylation site predictor based on a support vector machine algorithm (SVM), which allows studying the correlation between ubiquitylation and protein half-life. In order to overcome the lack of accuracy and training data deficiency, UbiProber [263] and iUbiq [264] were designed (Table 1). UbiProber predicts both general and species-specific ubiquitylation sites using large-scale experimental data as training set, while iUbiq is based on evolutionary information incorporated into the general form of pseudo-amino acid composition.…”

Progress and challenges in predicting protein methylation sites

“…For example, in 2013, our laboratory developed a species-specific ubiquitin conjugation sites predictor and the results of cross-species predictions indicated that species-specific prediction can effectively promote the prediction performance of ubiquitin sites. 41 Another recent study presented a novel approach to predict species-specific lysine acetylation sites across six different species and their results justified the necessity and importance of developing species-specific models to improve the prediction of lysine acetylation sites. 76 While due to the limited data available before, the regulation mechanism of methylation among different species were not discussed in the field of methylation sites prediction.…”