Computational identification of ubiquitylation sites from protein sequences

Background: Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites. Results: We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, k-nearest neighbor, and Na

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Chun-Wei Tung, Shinn-Ying Ho
• Keywords: BMCBI 2008, Physicochemical Properties, Putative Non-ubiquitylation Sites, Ubiquitylation Sites