| Summary: | 博士 === 元智大學 === 資訊工程學系 === 103 === In eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. The recent advancements in proteomics technology have stimulated an increasing amount of interest in identifying ubiquitin-conjugation sites. However, at the moment, most methods and computational prediction tools for ubiquitin-conjugation sites are focused on small-scale data. As more and more experimental data on ubiquitin conjugatation sites become available, it becomes possible to develop prediction models that can be scaled to big data. Therefore, we propose an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities. Moreover, in order to provide meaningful assistance to researchers interested in large-scale proteome data, the proposed models have been implemented into a web-based system (UbiSite), which is freely available at http://csb.cse.yzu.edu.tw/UbiSite/.
In addition, due to the very important roles of E3 ligases by recognizing specific protein substrate and catalyzing the attachment of ubiquitin to the target protein, the investigation of the networks of E3 ligases and ubiquitinated substrate proteins is emerging as a hot topic. However, there is a lack of methods proposed and tools designed to explore the regulatory networks of E3 ligases for ubiquitinated proteins. Therefore, in this work, we propose a method which applies support vector machine, graph theory and integrates all available ubiquitinome datasets, experimentally verified E3 ligases, and protein-protein interactions. Besides, UbiNet, a comprehensive web-resource is implemented to efficiently explore and provide a full investigation of protein ubiquitination networks. The current database of UbiNet contains: 499 experimentally verified E3 ligases, 43948 experimentally verified ubiquitination sites from 14692 ubiquitinated proteins of humans, and 41889 protein-protein interactions, and various relative information supporting for the exploring ubiquitination networks. The UbiNet is now freely accessible via http://csb.cse.yzu.edu.tw/UbiNet/.
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