2009 Fiscal Year Final Research Report
Prediction of the Protein O-glycosylation site by Neural Networks and Support Vector Machine
| Project/Area Number |
19300080
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
NISHIKAWA Ikuko Ritsumeikan University, 情報理工学部, 教授 (90212117)
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| Co-Investigator(Kenkyū-buntansha) |
ITO Masahiro 立命館大学, 生命科学部, 准教授 (50388112)
CHIN Eni 立命館大学, 情報理工学部, 教授 (60236841)
SAKAKIBARA Kazutoshi 立命館大学, 情報理工学部, 講師 (30388110)
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| Project Period (FY) |
2007 – 2009
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| Keywords | サポート / ベクタ / マシン / O型糖鎖修飾 / 機械学習 / 哺乳類タンパク質 / 天然変性領域 / 膜貫通タンパク質 / ニューラルネットワーク |
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| Research Abstract |
O-Glycosylation sites in the mammalian protein is predicted by the machine learning to elucidate the glycosylation mechanisms. Especially, the crowded and the isolated glycosylation sites are separately considered, and it is found that the former is affected by the amino acid composition of the neighboring sites while the latter is affected by the amino acid sequence, and more detailed characteristics are investigated. O-Glycosylation sites are often found in the intrinsically disordered proteins, which lead to the biological understandings of the conservation property, the role in the structural stability and so on. Biological experiments are also executed for a verification of the prediction.
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[Journal Article] Sphingomyelins in Four Ascidians, Ciona intestinalis, Halocynthia roretzi, Halocynthia aurantium, and Styela clava2009
Author(s)
Masahiro Ito, Kazuhito Yokoi, Takashi Inoue, Shogo Asano, Rei Hatano, Ryota Shinohara, Saki Itonori, Mutsumi Sugita
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Journal Title
J. Oleo Sci. 58(9)
Pages: 473-480
Peer Reviewed
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