| Project/Area Number |
15K06970
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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 |
Structural biochemistry
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| Research Institution | Osaka University |
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Principal Investigator |
Kobayashi Naohiro 大阪大学, たんぱく質研究所, 特任准教授(常勤) (80272160)
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| Co-Investigator(Kenkyū-buntansha) |
児嶋 長次郎 横浜国立大学, 大学院工学研究院, 教授 (50333563)
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| Co-Investigator(Renkei-kenkyūsha) |
HIROAKI Hidekazu 名古屋大学, 創薬科学研究科, 教授 (10336589)
MOTONO Chie 産業技術総合研究所, 創薬分子プロファイリング研究センター, 主任研究員 (80415752)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 核磁気共鳴法 / データベース / 自動解析 / ＮＭＲ / ホモロジーモデリング / NMR / モデリング |
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| Outline of Final Research Achievements |
The three-dimensional structure encoded in the genome sequence experimentally determined is about 10% of the total, and recently there are tendencies for cases of drug discovery research using a structure prediction model. In this research project, we have constructed a drug discovery support database that links a model structure database that can be a target of nuclear magnetic resonance (NMR) research, which is lined to several external life science databases such as BMRB, a database of NMR experiment data. In the period from FY2016 to FY2017, utilizing a database, we designed an automatic analysis pipeline based on deep learning, and determined the NMR signal assignments and the solution tertiary structure required for drug discovery research using about 25% of NMR data sets. The new technology has sucessfully shortened automatic NMR analysis to about half a day.
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