Relationship between activity and groups of metabolic pathways
| Project/Area Number |
16K07223
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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 |
System genome science
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
Shigehiko Kanaya 奈良先端科学技術大学院大学, 先端科学技術研究科, 教授 (90224584)
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| Project Period (FY) |
2016-10-21 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 代謝マップ / グラフコンボリューションネットワーク / 深層学習 / 二次代謝物質 / アルカロイド / 生合成 / バイオインフォマティクス / データサイエンス / 部分環骨格構造 / 代謝パスウエイ / クラスター分析 / 代謝モジュール / 生体生命情報学 / ゲノム / 生物・生体工学 |
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| Outline of Final Research Achievements |
In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network and examined the relationships between activities and metabolites based on metabolic pathways. In order to investigate alkaloid biosynthesis, we trained the network to distinguish the precursors of 566 alkaloids, which are almost all of the alkaloids whose biosynthesis pathways are known, and showed that the model could predict starting substances with an averaged accuracy of 97.5%. The prediction of pathways contributes to understanding of the alkaloids and the application of graph based neural network models to similar problems in bioinformatics would therefore be beneficial. We applied our model to evaluate the precursors of biosynthesis of 18000 alkaloids found in natural organisms and found some rules of relationships between chemical structure and activity.
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| Academic Significance and Societal Importance of the Research Achievements |
文献調査をもとに、640種のアルカロイドを中心に３０枚に及ぶ二次代謝経路マップを整理しウエブにより公開した。本代謝経路で、それぞれのアルカロイドの生合成開始物質を明確に確認できる。また、現在までに、12000種のアルカロイド化合物のうち深層学習により、10,051種について、生合成開始物質が予測でき、二次代謝経路予測における基盤となるデータを整備し、生物活性の関係も把握できる公開データベースを構築した。アルカロイド化合物の生合成開始物質の推定が可能になり、二次代謝研究における学術的意義が非常に高いことを示す。また、データを一般公開しており、社会的にも十分な貢献を果たしている。
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Report
(4 results)
Research Products
(13 results)
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[Journal Article] An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease2018
Author(s)
Eguchi, R., Karim, M. B., Hu, P., Sato, T., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M.
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Journal Title
BMC bioinformatics
Volume: 19(1)
Issue: 1
Pages: 264-264
DOI
Related Report
Peer Reviewed
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[Journal Article] Molecular Components of Arabidopsis Intact Vacuoles Clarified with Metabolomic and Proteomic Analyses2018
Author(s)
Miwa Ohnishi, Aya Anegawa, Yuko Sugiyama, Kazuo Harada, Akira Oikawa, Yasumune Nakayama, Fumio Matsuda, Yukiko Nakamura, Ryosuke Sasaki, Chizuko Shichijo Patrick G. Hatcher, Hidehiro Fukaki, Shigehiko Kanaya, Koh Aoki, Mami Yamazaki, Eiichiro Fukusaki, Kazuki Saito, Tetsuro Mimura
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Journal Title
Plant and Cell Physiology
Volume: 69
Pages: 1353-1362
DOI
Related Report
Peer Reviewed / Int'l Joint Research
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[Journal Article] Big Data and Network Biology 20162016
Author(s)
Shigehiko Kanaya, Md Altaf-Ul-Amin, Samuel K Kiboi, Farit Mochamad Afendi
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Journal Title
BioMed research international
Volume: 2017
Pages: 1-2
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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