Prediction of GPI-anchored protein using position-specific scoring matrix and depelopment of GPI-anchored protein detabase
| Project/Area Number |
24700298
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | Meiji University |
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Principal Investigator |
IKEDA Yuri 明治大学, 理工学部, 准教授 (30371082)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | バイオインフォマティクス / タンパク質 / 翻訳後修飾 / GPIアンカー / PSSM / 機械学習 / データベース / ゲノムスキャン |
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| Research Abstract |
In this study, new methods for the detection of GPI-anchored proteins (GPI-APs) and for the prediction of the location of omega-sites by physicochemical properties, PSSM and back-propagation artificial neural networks (BP-ANNs) were developed. PSSs were calculated based on amino acid propensities around the omega-sites. PSSs were applied to BP-ANNs which consist of a three-layered structure. This method could distinguish GPI-APs from non-GPI-APs (92.9% sensitivity and 94.8% specificity) and also could discriminate omega-sites from non-omega-sites with higher accuracy (95.6% sensitivity and 97.0% specificity) than other GPI-APs detection tools reported previously.
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Report
(3 results)
Research Products
(71 results)
