| Project/Area Number |
15K16081
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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 |
Life / Health / Medical informatics
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Ohue Masahito 東京工業大学, 情報理工学院, 助教 (50743209)
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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 |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | ペプチド創薬 / タンパク質間相互作用 / バイオインフォマティクス / 分子ドッキング / 標的予測 / カーネル法 / ランク学習 / 中分子 / アミノ酸配列 / 構造インフォマティクス / MEGADOCK-Web / ペプチド分子 / MEGADOCK / プロファイル / 中規模ペプチド / アミノ酸相互作用プロファイル |
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| Outline of Final Research Achievements |
The purpose of this study is to efficiently search for peptide targets, which is considered important in middle molecule drug discovery, and to develop a prediction method reveals binding target.
I have performed large-scale parallelization to predict protein-protein interactions based on three-dimensional structure information. In addition, an all-to-all prediction was performed on representative human protein structures, and an web-interface was constructed to find the prediction results via a web browser. In addition, I have developed computational methods that conducting binding analysis of peptide molecules and proteins, LIK method which is a molecular target prediction method based on bipartite graph prediction by machine learning, and another molecular target prediction method named PKRank based on learning-to-rank.
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