NMR AND DATA REDUCTION TECHNIQUES FOR LARGE BIOMOLECULES

大生物分子的核磁共振和数据简化技术

• 批准号: 6271783
• 负责人: GERHARD WAGNER
• 金额: $ 13.29万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6271783
• 关键词: chemical aggregate; chemical models; computer data analysis; computer program /software; computer simulation; data collection; macromolecule; mathematical model; method development; nuclear magnetic resonance spectroscopy; nucleic acid structure; protein structure; stable isotope

We plan to develop NMR and computational methods for structural studies of large proteins. This will include further development of multidimensional multiple resonance techniques for conformation-independent sequential assignments and for measurements of homonuclear and heteronuclear coupling constants. This may lead to a better characterization of dihedral angles, side chain orientations and orientations of the peptide planes. This will improve the possibility of identifying hydrogen bonds and may lead to higher quality structures of large proteins. These techniques require precise measurements of resonance positions. We therefore propose proton decoupling schemes that will increase resolution and sensitivity in heteronuclear multidimensional NMR spectra. Furthermore, we will extensively explore methods of processing truncated data sets, examine and apply non-linear sampling techniques and develop processing software that can handle such data sets. To make use of the increased resolution, large multidimensional data sets will have to be handled. At present, this is extremely difficult due to limitations of computing speed and disk space. Consequently, we are in a situation where high resolution and high information content obtainable with advanced NMR technology must be sacrificed due to limitations of the computational facilities. We therefore propose to purchase a fast parallel computer with a large data storage device and to port/develop NMR processing, analysis and other related software. Analysis of multidimensional NMR spectra requires extensive paging through 2D cross planes of 3D or 4D spectra. We plan to develop a mask convolution technique that will lead to a quick and robust assignment of cross peaks. This will be accompanied by development of automated assignment techniques in multidimensional NMR spectra. Finally, to direct the effort in measurements of structural parameters, we will perform simulations to estimate the impact of precise determination of homonuclear and heteronuclear vicinal coupling constants relative to and in addition to measuring cross relaxation rates precisely and in large. To improve the latter aspect we will adapt and further develop an iterative strategy proposed by Koehl and Lefevre (1990) for the separation of direct cross relaxation and spin diffusion.

我们计划开发NMR和计算方法，用于结构研究， 大蛋白质 这将包括进一步发展多层面的 多重共振技术 分配和用于测量同向和异向耦合 constants. 这可以导致二面角的更好表征， 侧链取向和肽平面的取向。 这将 提高识别氢键的可能性，并可能导致 更高质量的大蛋白质结构。 这些技术需要 精确测量谐振位置。 因此，我们建议质子 解耦方案，将提高分辨率和灵敏度， 异向多维NMR谱。 此外，我们将 广泛探索处理截断数据集的方法，检查和 应用非线性采样技术，开发处理软件， 可以处理这样的数据集。 为了利用增加的分辨率， 必须处理多维数据集。 目前，这是 由于计算速度和磁盘空间的限制，这是非常困难的。 因此，我们处于高分辨率和高分辨率的情况下， 利用先进的NMR技术获得的信息内容必须 由于计算设备的限制而牺牲。 我们 因此，我建议购买一台高速并行计算机， 存储设备和端口/开发NMR处理、分析和其他 相关软件。 多维NMR谱的分析需要 通过3D或4D光谱的2D交叉平面的广泛分页。 我们计划 开发一种掩模卷积技术， 交叉峰的分配。 这将伴随着发展 多维NMR谱的自动归属技术。 最后， 为了指导结构参数的测量工作，我们将 进行模拟，以估计精确确定 同邻和异邻耦合常数 除了精确地和大范围地测量交叉弛豫速率之外。 到 改进后一个方面，我们将适应并进一步开发一个迭代 Koehl和Lefevre（1990年）提出的分离直接 交叉弛豫和自旋扩散。