Hyper-dimensional NMR spectroscopy for automated protein structure determination

用于自动化蛋白质结构测定的超维核磁共振波谱

• 批准号: 140821744
• 负责人: Dr. Victor Jaravine
• 金额: --
• 依托单位: Institut für Biophysikalische Chemie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2009
• 资助国家: 德国
• 起止时间: 2008-12-31 至 2012-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/140821744?language=en
• 关键词: Hyper; dimensional; NMR; spectroscopy; automated

Adequate spectral resolution is essential for the study of biomacromolecules by NMR, especially for proteins that are either large, unfolded, or membrane-bound. Using conventional methods, the analysis of spectra is greatly complicated by signal overlap. To address this problem we capitalize on our recent breakthroughs in NMR and computational methods: real-time (Jaravine and Orekhov 2006) fast spectroscopy of any dimensionality (Jaravine et al. 2006), and fully automated spectra analysis (Lopez-Mendez and Guntert 2006). Our R-MDD algorithm (Jaravine et al. 2006) can deliver highest spectral resolution affordable by spin magnetization lifetimes in the indirect dimensions. Recently, we have shown how this increased resolution facilitates and improves the accuracy of the assignment for a set of backbone experiments (Jaravine et al. 2008). Here we propose the extension of Hyper-Dimensional NMR methodology (Jaravine et al. 2008) to new applications in biomolecular NMR, in particular, for automated side-chain assignment and structure calculation. The possibility to enhance the resolution in NOESY experiments for the collection of conformational restraints (Luan et al. 2005) is especially promising in combination with fully automated structure calculation with the FLYA algorithm (Lopez-Mendez and Guntert 2006; Scott et al. 2006). A second direction of this project is the use of Hyper-Dimensional NMR methodology for the joint interpretation of information from the most sensitive experiments for resolution enhancement in the corresponding dimensions of less sensitive experiments. This research project will show how to achieve optimizations with respect to measurement time, resolution, sensitivity, data completeness, and accuracy. Applications of the fully automated approaches to the 23 kDa protein RcsD_HPt shall demonstrate the power of the method for answering questions in structural biology.

适当的光谱分辨率对于用核磁共振研究生物大分子是至关重要的，特别是对于大的、未折叠的或膜结合的蛋白质。用常规方法分析光谱时，由于信号重叠，使得频谱分析非常复杂。为了解决这个问题，我们利用了我们最近在核磁共振和计算方法方面的突破：实时(Jaravine和Orekhov，2006)任意维度的快速光谱(Jaravine等人。以及全自动光谱分析(Lopez-Mendez和Guntert，2006)。我们的R-MDD算法(Jaravine等人2006)可以提供间接维度中自旋磁化寿命所能承受的最高光谱分辨率。最近，我们已经展示了这种提高的分辨率如何促进和提高一组骨干实验的任务的准确性(Jaravine等人。2008年)。在这里，我们建议扩展超维核磁共振方法(Jaravine等人。2008)涉及生物分子核磁共振的新应用，特别是用于自动侧链分配和结构计算。提高NOESY实验中收集构象限制的分辨率的可能性(Luan等人)。2005)与使用FLYA算法的全自动结构计算相结合尤其有希望(Lopez-Mendez and Guntert 2006；Scott et al.2006)。该项目的第二个方向是使用超维核磁共振方法联合解释来自最敏感的实验的信息，以提高不太敏感的实验的相应维度的分辨率。本研究项目将展示如何在测量时间、分辨率、灵敏度、数据完整性和准确性方面实现优化。将全自动方法应用于23 kDa蛋白质RCSD_HPT，将展示该方法在结构生物学中回答问题的能力。

项目成果

A Universal Expression Tag for Structural and Functional Studies of Proteins

用于蛋白质结构和功能研究的通用表达标签

• DOI: 10.1002/cbic.201200045
• 发表时间: 2012
• 期刊: ChemBioChem
• 影响因子: 3.2
• 作者: Rozenknop;Rogova;Tikole;Jaravine;Güntert;Dötsch
• 通讯作者: Dötsch

Blind testing of routine, fully automated determination of protein structures from NMR data.

• DOI: 10.1016/j.str.2012.01.002
• 发表时间: 2012-02-08
• 期刊: STRUCTURE
• 影响因子: 5.7
• 作者: Rosato, Antonio;Aramini, James M.;Arrowsmith, Cheryl;Bagaria, Anurag;Baker, David;Cavalli, Andrea;Doreleijers, Jurgen F.;Eletsky, Alexander;Giachetti, Andrea;Guerry, Paul;Gutmanas, Aleksandras;Guentert, Peter;He, Yunfen;Herrmann, Torsten;Huang, Yuanpeng J.;Jaravine, Victor;Jonker, Hendrik R. A.;Kennedy, Michael A.;Lange, Oliver F.;Liu, Gaohua;Malliavin, Therese E.;Mani, Rajeswari;Mao, Binchen;Montelione, Gaetano T.;Nilges, Michael;Rossi, Paolo;van der Schot, Gijs;Schwalbe, Harald;Szyperski, Thomas A.;Vendruscolo, Michele;Vernon, Robert;Vranken, Wim F.;de Vries, Sjoerd;Vuister, Geerten W.;Wu, Bin;Yang, Yunhuang;Bonvin, Alexandre M. J. J.
• 通讯作者: Bonvin, Alexandre M. J. J.