2022 Fiscal Year Final Research Report
Use of contact prediction-based restraints for protein structure determination from sparse NMR data
| Project/Area Number |
20K06508
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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 43020:Structural biochemistry-related
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
GUENTERT Peter 東京都立大学, 理学研究科, 客員教授 (20392110)
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| Co-Investigator(Kenkyū-buntansha) |
池谷 鉄兵 東京都立大学, 理学研究科, 准教授 (30457840)
伊藤 隆 東京都立大学, 理学研究科, 教授 (80261147)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | machine learning / NMR / protein structure / automated assignment |
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| Outline of Final Research Achievements |
We have incorporated the use of protein structures predicted by AlphaFold2 into our fully automated NMR spectra analysis algorithm ARTINA, which yields peak lists, chemical shift assignments, and three-dimensional protein structures directly from a set of multidimensional NMR spectra without any manual work. The AlphaFold2 structures can be used in ARTINA for the structure-based prediction of approximate chemical shifts and for generating the cross peaks expected in NOESY-type spectra. It could be shown that the AlphaFold2 structures enable to obtain reliable chemical shift assignments from smaller sets of NMR spectra than without structures. Thus, NMR measurement times can be significantly reduced and the NMR studies of proteins becomes more efficient. The ARTINA algorithm has been made available in the NMRtist webserver that allows scientists to obtain assignments and structures of proteins within a few hours of computation time rather than weeks or months of manual analysis.
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| Free Research Field |
Biophysical Chemistry
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| Academic Significance and Societal Importance of the Research Achievements |
Nuclear magnetic resonance spectroscopy (NMR) provides detailed information on structure, dynamics and interactions of proteins. The method developed in this project will accelerate virtually any biological NMR studies that require the analysis of protein NMR spectra and chemical shift assignments.
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