2013 Fiscal Year Final Research Report
Prediction of the post-translational modification sites of the protein by machine learning to study the modification mechanism
| Project/Area Number |
23500372
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Keywords | 機械学習 / タンパク質 / 天然変性領域 / リン酸化 / 予測 / サポートベクターマシン / 進化的保存度 |
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| Research Abstract |
Phosphorylation is one of the most important post-translational modifications of the protein. Phosphorylation sites of the human protein are predicted using the machine learning by support vector machine (SVM). SVMs are constructed for prediction target sties in the functional domain and in the intrinsically disordered (ID) region, separately. As the result, human amino acid sequence information is enough for the effective prediction for the domain, while the site specific evolutionary conservation information turns out to be effective for ID. That is, conservation rate is not uniform in ID, where the sequence conservation is known to be relatively low in general. Site specific conservation is newly defined based on the ortholog proteins, and the conservation rate is high at the phosphorylation sites, especially at the functional phosphorylation sites, therefore which is effective for the prediction.
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Research Products
(28 results)
