| 研究課題/領域番号 |
23K05660
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分43020:構造生物化学関連
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| 研究機関 | 東京都立大学 |
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研究代表者 |
PETER GUENTERT 東京都立大学, 理学研究科, 客員教授 (20392110)
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| 研究分担者 |
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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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,810千円 (直接経費: 3,700千円、間接経費: 1,110千円)
2025年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2024年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2023年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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| キーワード | NMR / machine learning / automated assignment / protein structure / protein / spectra analysis / ARTINA |
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| 研究開始時の研究の概要 |
Using generated training data, we will apply deep learning for (i) a better treatment of time-domain data acquired with non-uniform sampling (NUS) than conventional approaches, (ii) resolution enhancement by virtual decoupling, and (iii) deconvolution of highly overlapped signals. In this way, time-consuming or costly experimental methods for obtaining better NMR spectra can be replaced by computational alternatives, thereby increasing the efficiency, completeness, and quality of NMR spectra analysis.
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| 研究実績の概要 |
The ARTINA workflow for machine learning-based automated NMR spectra analysis was combined with AlphaFold structure prediction and UCBShift chemical shift prediction in order to drastically reduce the amount of NMR spectra that are required for obtaining the chemical shift assignment of a protein. Extensive studies have been performed to identify the optimal sets of NMR spectra for the assignment of the backbone or all chemical shifts in a protein. This was published in Klukowski et al., Science Advances 9, eadi9323 (2023).
In addition, ARTINA was generalized to additional biomacromolecular systems. Originally, ARTINA was designed exclusively for monomeric proteins composed of standard amino acid residues. This restriction has been lifted to enable automated NMR spectra analysis also for protein-protein, protein-small molecule ligand, RNA and DNA systems. This significantly extends the applicability of the method to biologically important systems.
A large scale data set comprising more than 1300 multidimensional NMR spectra, from which the chemical shift assignments and three-dimensional structures of 100 proteins can be obtained, has been published as open research data for general use by the NMR research community. Published in Klukowski et al., Nature Scientific Data 11, 30 (2024).
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Research progressed as planned. In particular, we started to develop new machine learning models specifically for analyzing two-dimensional homonuclear NMR spectra of proteins (see below).
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| 今後の研究の推進方策 |
As a new direction of research, we have started to implement new machine learning models specifically for the purpose of analyzing two-dimensional 1H-1H NMR spectra of proteins up to ~20 kDa. If successful, this will enable efficient NMR studies of proteins without isotope labeling and requiring much less NMR measurement time.
Training and testing data is crucial for machine learning applications, which constitutes a limiting factor for its use in biological NMR spectroscopy. In order to collect and make available a larger and more diverse set of multi-dimensional NMR spectra, we are developing a new public website and data repository for the upload, storage, and access of primary biomolecular NMR data, i.e. spectra or time-domain data. This should become available for general use by the NMR research community in the near future.
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