A new scheme to discover functional associations and regulatory networks of E3 ubiquitin ligases

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .

Towards Computational Models of Identifying Protein Ubiquitination Sites

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network

The ubiquitination mediated by ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) cascade is crucial to protein degradation, transcription regulation, and cell signaling in eukaryotic cells. The high specificity of ubiquitination is regulated by the interaction between E3 ubiquitin ligases and their target substrates. Unfortunately, the landscape of human E3-substrate network has not been systematically uncovered. Therefore, there is an urgent need to develop a high-throughput and efficient strategy to identify the E3-substrate interaction. To address this challenge, we develop a computational model based on multiple types of heterogeneous biological evidence to investigate the human E3-substrate interactions. Furthermore, we provide UbiBrowser as an integrated bioinformatics platform to predict and present the proteome-wide human E3-substrate interaction network ( http://ubibrowser.ncpsb.org ).Protein stability modulation by E3 ubiquitin ligases is an important layer of functional regulation, but screening for E3 ligase-substrate interactions is time-consuming and costly. Here, the authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.

An α-quaternary chiral latam derivative, YH-304 as a novel broad-spectrum anticancer agent

Previously, we reported that α-quaternary chiral lactam derivatives have broad spectrum anticancer activity. However, the underlying molecular mechanisms and its relevance are largely unknown. In the present study, we report progress on α-quaternary chiral lactam analogues that address this, focusing on the novel analogue YH-304 as a candidate to broadly target human cancer cells. The effect of YH-304 on cell transformation was assessed by clonogenic assay in non-small cell lung cancer cells (NSCLCs) A549 and 226B. Proapoptotic activity of YH-304 was determined by TUNEL assay and cleaved PARP, cleaved caspase-9, and Bax as markers for apoptosis. The p53-dependency and therapeutic spectrum of YH-304 was assessed by western blot analysis, real-time PCR, and cell viability assays in cells expressing endogenous wild or mutant p53. The effect of YH-304 on angiogenesis in vivo was examined by bFGF-mediated angiogenesis assay in zebrafish. Finally, the effect of YH-304 on AKT and ERK activation (phosphorylation) as a putative mechanism underlying the effect of YH-304 on bFGF-mediated angiogenesis was assessed using western blotting. We found that YH-304 significantly decreases the colony-forming activities of both A549 and 226B cells, inducing cellular apoptosis. Unlike nutlin-3 (p53 pathway activator), YH-304 did not affect the expression levels of p53 and its target gene such as p21 and thus showed p53-independent anticancer activity with broad spectrum. In addition, YH-304 inhibited bFGF-induced angiogenesis in vivo through mediating AKT and ERK signaling pathway, which plays an important role in bFGF activation and angiogenesis. Taken together, our data indicate that YH-304 may represent a novel therapeutic option for the treatment of cancer in a p53-independent manner.