2020 Fiscal Year Final Research Report
Development of siginal processing and proten structure determination methods for low qulaity NMR data
Project/Area Number |
18K06160
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 43040:Biophysics-related
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Research Institution | Tokyo Metropolitan University |
Principal Investigator |
IKEYA Teppei 東京都立大学, 理学研究科, 助教 (30457840)
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Project Period (FY) |
2018-04-01 – 2021-03-31
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Keywords | NMR / 蛋白質 / 立体構造計算 |
Outline of Final Research Achievements |
We achieved the first de novo protein structure determinations in eukaryotes with the sf9 cell that are based exclusively on NMR data from living cells. As model systems, we applied this method to three proteins and determined well-converged 3D structures. A new method for structure determination with Bayesian inference was particularly effective for determining 3D structures with sufficient precision to detect conformational differences between the structures in sf9 cells and in diluted solution. We also developed another new NMR protein structure determination method for the inference of multi-state conformations using multiple types of NMR data. Applying the method to the protein YUH1, we find large dynamics of two loops surrounding the active site for ubiquitin-recognition and proteolysis. Our results, including those from biochemical analysis, showed that large motion surrounding the active site contributes strongly to the efficiency of the enzymatic activity.
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Free Research Field |
生物物理
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Academic Significance and Societal Importance of the Research Achievements |
創薬研究分野では,蛋白質立体構造情報を利用した構造基盤薬物設計(Structural Based-Drug Discovery; SBDD)が,効率的な薬剤設計法として確立している.本課題のアンサンブル構造解析が確立できれば,生体分子の揺らぎを含めた形で,蛋白質ー低分子化合物,蛋白質間相互作用等を解析可能となり,こうした情報の蓄積によりSBDD研究にも全く新しい知見を提供できる.また,ここで開発した統計解析のソフトウェアの世界への普及、全世界の生体系NMRのグループが本技術を共有できるよう世界に還元していく.
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