| Project/Area Number |
23500373
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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 | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
NAGANO Nozomi 独立行政法人産業技術総合研究所, 生命情報工学研究センター, 主任研究員 (70357648)
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| Co-Investigator(Kenkyū-buntansha) |
KATO Tsuyoshi 群馬大学, 工学研究科, 准教授 (40401236)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 酵素 / 活性部位 / 予測法 / 重みつき偏差 / カーネル / 補酵素 / 構造 / 酵素反応 |
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| Research Abstract |
Prediction of active sites in enzyme proteins is extremely essential not only for protein sciences but also for practical applications. Because enzyme reaction mechanisms are based on the local structures of enzyme active sites, a simple measurement, mean square deviation, has been used to compare such local structures in proteins so far. In order to improve the ability of such a simple measurement, various kinds of template-based methods that compare the local sites have been developed to date. In this work, parameters for the deviation was introduced. Moreover, the Bregman Divergence Regularized Machine was also employed to develop a new machine learning algorithm that determines the parameters of the square deviation. Experimental results showed that the proposed methods possess promising search performance.
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