Dynamics of protein-protein interface: Development of analysis methods based on machine learning and MD simulation
| Project/Area Number |
18K05025
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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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| Review Section |
Basic Section 32010:Fundamental physical chemistry-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Yamashita Takefumi 東京大学, 先端科学技術研究センター, 特任准教授 (50615622)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 分子動力学 / 機械学習 / タンパク質間相互作用 / タンパク質 / 抗体 / シミュレーション / 分子動力学シミュレーション / 古典力学 / データベース / 分子ダイナミクス / ２次元カオス系 / 深層学習 / タンパク質-タンパク質相互作用 |
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| Outline of Final Research Achievements |
Due to the development of machine learning technology, artificial intelligence has surpassed human intelligence in areas such as image recognition, chess, and Go. In this study, we aim to introduce such machine learning techniques into molecular science to obtain deeper insight into the characteristic dynamics of the protein systems. In this study, we first developed a machine learning technique to predict the dynamics of a simple model system. We gradually increased the complexity of the target system and eventually succeeded in developing a machine learning technique to analyze the antigen-antibody interface.
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| Academic Significance and Societal Importance of the Research Achievements |
機械学習技術により、人間が認識できなかったものを認識することが可能になってきた。本研究では、抗体と抗体に捕えられた抗原の間の界面を、機械学習を活用して解析する技術の開発に成功した。抗原-抗体界面の複雑なダイナミクスの理解が深まると、抗原と強く結合できる抗体の設計が容易になると期待される。この研究成果は、分子と分子の複雑な関係性を紐解くと言う学術的意義だけでなく、抗体医薬品の設計にも役立つ可能性がある。
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Report
(6 results)
Research Products
(26 results)
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[Journal Article] Multiplicity in Long Noncoding RNA Biomedical Sciences2018
Author(s)
3.R. Kurokawa, R. Komiya, T. Oyoshi, Y. Matsuno, H. Tani, M. Katahira, K. Hitachi, Y. Iwashita, T. Yamashita, K. Kondo, R. Yoneda, Y. Yamaoki, N. Ueda, T. Mashima, N. Kobayashi, T. Nagata, A. Kiyoishi, M. Miyake, F. Kano, M. Murata1, N. Hamad, K. Sasaki, N. Shoji
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Journal Title
Biomedical Sciences
Volume: 4
Issue: 2
Pages: 18-23
DOI
Related Report
Peer Reviewed / Open Access
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