Support Vector Machine Prediction of N- and O-glycosylation Sites Using Whole Sequence Information and Subcellular Localization

• Item type: Trans(1)
• 公開日: 2009-03-24
• タイトル: Support Vector Machine Prediction of N- and O-glycosylation Sites Using Whole Sequence Information and Subcellular Localization
• タイトル / 言語: en
• タイトル: Support Vector Machine Prediction of N- and O-glycosylation Sites Using Whole Sequence Information and Subcellular Localization
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Papers
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Department of Biosciences and Informatics, Keio University
• 著者所属: Department of Biosciences and Informatics, Keio University
• 著者所属: Department of Biosciences and Informatics, Keio University
• 著者所属(英): en
• 著者所属(英): Department of Biosciences and Informatics, Keio University
• 著者所属(英): en
• 著者所属(英): Department of Biosciences and Informatics, Keio University
• 著者所属(英): en
• 著者所属(英): Department of Biosciences and Informatics, Keio University
• 著者名: Kenta, Sasaki; Nobuyoshi, Nagamine; Yasubumi, Sakakibara
• 著者名(英): Kenta, Sasaki; Nobuyoshi, Nagamine; Yasubumi, Sakakibara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Background: Glycans, or sugar chains, are one of the three types of chain (DNA, protein and glycan) that constitute living organisms; they are often called "the third chain of the living organism". About half of all proteins are estimated to be glycosylated based on the SWISS-PROT database. Glycosylation is one of the most important post-translational modifications, affecting many critical functions of proteins, including cellular communication, and their tertiary structure. In order to computationally predict N-glycosylation and Oglycosylation sites, we developed three kinds of support vector machine (SVM) model, which utilize local information, general protein information and/or subcellular localization in consideration of the binding specificity of glycosyltransferases and the characteristic subcellular localization of glycoproteins. Results: In our computational experiment, the model integrating three kinds of information achieved about 90% accuracy in predictions of both N-glycosylation and O-glycosylation sites. Moreover, our model was applied to a protein whose glycosylation sites had not been previously identified and we succeeded in showing that the glycosylation sites predicted by our model were structurally reasonable. Conclusions: In the present study, we developed a comprehensive and effective computational method that detects glycosylation sites. We conclude that our method is a comprehensive and effective computational prediction method that is applicable at a genome-wide level.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Background: Glycans, or sugar chains, are one of the three types of chain (DNA, protein and glycan) that constitute living organisms; they are often called "the third chain of the living organism". About half of all proteins are estimated to be glycosylated based on the SWISS-PROT database. Glycosylation is one of the most important post-translational modifications, affecting many critical functions of proteins, including cellular communication, and their tertiary structure. In order to computationally predict N-glycosylation and Oglycosylation sites, we developed three kinds of support vector machine (SVM) model, which utilize local information, general protein information and/or subcellular localization in consideration of the binding specificity of glycosyltransferases and the characteristic subcellular localization of glycoproteins. Results: In our computational experiment, the model integrating three kinds of information achieved about 90% accuracy in predictions of both N-glycosylation and O-glycosylation sites. Moreover, our model was applied to a protein whose glycosylation sites had not been previously identified and we succeeded in showing that the glycosylation sites predicted by our model were structurally reasonable. Conclusions: In the present study, we developed a comprehensive and effective computational method that detects glycosylation sites. We conclude that our method is a comprehensive and effective computational prediction method that is applicable at a genome-wide level.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics （TBIO）; 巻 2, p. 25-35, 発行日 2009-03-24
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会