Support Vector Machine Prediction of N- and O-glycosylation Sites Using Whole Sequence Information and Subcellular Localization
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- Sasaki Kenta
- Department of Biosciences and Informatics, Keio University
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- Nagamine Nobuyoshi
- Department of Biosciences and Informatics, Keio University
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- Sakakibara Yasubumi
- Department of Biosciences and Informatics, Keio University
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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 O-glycosylation 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.
収録刊行物
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- IPSJ Transactions on Bioinformatics
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IPSJ Transactions on Bioinformatics 2 25-35, 2009
一般社団法人 情報処理学会
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詳細情報 詳細情報について
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- CRID
- 1390001205295504768
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- NII論文ID
- 110007990336
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- NII書誌ID
- AN00116647
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- ISSN
- 18827772
- 18826679
- 03875806
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- NDL書誌ID
- 024340247
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 使用不可