研究生(外文):Van-Nui Nguyen 論文名稱:的表徵 蛋白泛素化網站和E3連接酶調控網絡 論文名稱(外文):Characterization of protein ubiquitination sites and E3 ligase regulatory networks 指導教授:賴國華 指導教授(外文):K. Robert Lai 學位類別:博士 校院名稱:元智大學 系所名稱:資訊工程學系 語文別:英文 論文頁數:133 論文摘要
泛素結合作用是真核生物中相當重要的基礎機轉，主要與蛋白酶體降解相關。像是，透過該作用能夠調控細胞中多種的生物機制。近年來，有大量研究與泛素化作用發生位置相關；然而，大多數的計算方法及預測工具皆基於小規模資料來發展。由於泛素化作用位置之實驗資料數量與日俱增。在此契機下，有機會透過生物資訊技術來分析大規模泛素化作用實驗驗證資料，發展泛素化作用位置之預測模型。因此，我們提出利用疊代統計方法來辨認泛素化作用發生位置，探索該位置周圍是否具有序列特徵。除此之外，為了提供對於泛素化蛋白質體學有興趣的研究人員，更具生物意義上的協助，該方法同時於線上實現自動化分析「UbiSite」，免費提供服務http://csb.cse.yzu.edu.tw/UbiSite/。
另外，E3連接酶（E3Ligases）具有辨認特定泛素化受質之特性，以及催化泛素連接至受質之特定位置上。於是，調查E3連接酶與泛素化受質間的網路，逐漸成為一個熱門的研究題目。截至目前，尚缺乏旨在探索E3連接酶與泛素化蛋白間調控網路的方法及工具。因此，本研究將基於機器學習學與圖論學，提出一個整合實驗驗證泛素化蛋白、E3連接酶及蛋白質相互作用等資料之方法，來嘗試解決上述問題。「UbiNet」一個全面性的線上資源，將有效地探索蛋白質泛素化調控網路和提供相關泛素化蛋白質體資訊。目前UbiNet收錄：499個實驗驗證E3轉移酶、43948個實驗驗證泛素化位置，來自14692個人類泛素化蛋白質和41889筆蛋白質交互作用等資料，以及其他相關資訊用以建構泛素化調控網路，該服務已免費提供於http://csb.cse.yzu.edu.tw/UbiNet/.

論文外文摘要
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/.