2019 Fiscal Year Final Research Report
Development of protein function prediction methods with global substructures and interaction stochastic models
| Project/Area Number |
16K00392
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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 |
Life / Health / Medical informatics
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| Research Institution | Matsue National College of Technology (2017-2019)
Kyoto University (2016) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
小谷野 仁 国立研究開発法人農業・食品産業技術総合研究機構, 農業情報研究センター, 研究員 (10570989)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Keywords | タンパク質相互作用予測 / タンパク質二量体予測 / 中央文字列 / 文法圧縮 / 一般化Series-Parallelグラフ |
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| Outline of Final Research Achievements |
It is useful for understanding protein functions to identify biomolecules interacting with proteins and proteins consisted in a complex. We improved prediction methods for protein-RNA residue-base contacts and heterodimeric protein complexes. We developed an estimation method of a Laplace-like mixture on a set of strings, and enhanced the speed of finding median strings. Furthermore, we developed grammar-based compression methods for domain sequences of proteins and generalized series-parallel graphs.
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| Free Research Field |
生物情報学
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| Academic Significance and Societal Importance of the Research Achievements |
タンパク質相互作用およびタンパク質複合体の予測精度を向上させることによってタンパク質の機能を推定する手がかりとし，病原菌の感染経路の解明など生物学，医学へ貢献する．また文字列の集合上の確率分布や中央文字列の厳密解法，一般化Series-Parallelグラフの最小文法を与えることはタンパク質やRNAの配列解析や生物学的ネットワーク構造の解析に有用であるだけでなく数理学的にも意義がある．
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