High Throughput Protein NMR Structure via CLOUDS

通过云计算高通量蛋白质 NMR 结构

• 批准号: 6892905
• 负责人: MIGUEL LLINAS
• 金额: $ 25.93万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6892905
• 关键词: RNA; X ray crystallography; computer data analysis; computer simulation; high throughput technology; hydrogen ions; method development; molecular dynamics; nuclear magnetic resonance spectroscopy; protein folding; protein structure

DESCRIPTION (provided by applicant): In principle, NMR should be ideally suited for fast structural/functional screening of novel genomic modules. In practice, the analysis of protein NMR structural data is hampered by the assignment of the spectral signals to the individual spins (1H, 13C, 15N, etc.). Much effort is being devoted to expedite the assignment process. Progress stems mainly from the design of increasingly powerful heteronuclear multidimensional NMR experiments, interactive computer-based sorting of spin-spin scalar and Overhauser connectivities, and refinement from spin-spin dipolar couplings. In order to minimize the assignment bottleneck, we have developed a protocol, CLOUDS, that relies on a relaxation matrix-based analysis of nuclear Overhauser data (2D-NOESY spectra). A practical advantage of CLOUDS is that it leads to a computed location of unassigned distributed spins. A "structure" free of covalent linkages --in fact, a cloud of protons-- is thus obtained which represents the spatial location of the H-atoms relative to each other. By overlapping a large number (>1000) of such clouds, a family of clouds (foc) in 3D Cartesian space is generated that represents an effective proton density distribution. By fitting the linear polypeptide sequence to the foc proton density, a structure can be derived that is comparable to those obtained via traditional NMR methods. Here, we propose to further develop CLOUDS and enhance its capabilities for high throughput NMR structure elucidation. We propose to develop SPI, a program which automatically groups resonances into spin systems, based on 2D homonuclear and 3D 15N-edited data. We also propose a novel computational protocol, BACUS, for identifying resonances grouped according to a significant fraction of NOEs. BACUS does not require resonance assignments, thus addressing a main drawback to the NOE-based "direct" methods. BACUS is not related to any of the reported NOESY assignment protocols and, in principle, is applicable, not only for CLOUDS, but also within a conventional structural elucidation strategy. Finally, we propose to expand the protocol by incorporating other refinement criteria, such as dihedral angle constraints, residual dipolar couplings, anti-distance constraints, etc., to speed up the procedure and improve the CLOUDS- derived structures. A main objective of this project is the development of a robust computational protocol to obtain protein folds with minimal amounts of NMR experimental data.

描述（由申请人提供）：原则上，NMR 应非常适合新型基因组模块的快速结构/功能筛选。实际上，蛋白质 NMR 结构数据的分析因将光谱信号分配给各个自旋（1H、13C、15N 等）而受到阻碍。我们正在投入大量精力来加快分配过程。进展主要源于日益强大的异核多维核磁共振实验的设计、基于交互式计算机的自旋-自旋标量和奥弗豪瑟连通性排序，以及自旋-自旋偶极耦合的改进。为了最大限度地减少分配瓶颈，我们开发了一种协议 CLOUDS，它依赖于核 Overhauser 数据（2D-NOESY 谱）的基于松弛矩阵的分析。云的一个实际优点是它可以计算出未分配的分布式自旋的位置。由此获得一种没有共价键的“结构”——实际上是质子云——它代表了氢原子相对于彼此的空间位置。通过重叠大量（>1000）这样的云，可以生成 3D 笛卡尔空间中的云族 (foc)，它代表有效的质子密度分布。通过将线性多肽序列与焦点质子密度进行拟合，可以获得与通过传统 NMR 方法获得的结构相当的结构。在这里，我们建议进一步开发 CLOUDS 并增强其高通量 NMR 结构解析的能力。我们建议开发 SPI，这是一个基于 2D 同核和 3D 15N 编辑数据自动将共振分组到自旋系统中的程序。我们还提出了一种新颖的计算协议 BACUS，用于识别根据大部分 NOE 分组的共振。 BACUS 不需要共振分配，从而解决了基于 NOE 的“直接”方法的主要缺点。 BACUS 与任何已报道的 NOESY 分配协议无关，原则上不仅适用于云，而且适用于传统的结构阐明策略。最后，我们建议通过合并其他细化标准（例如二面角约束、残余偶极耦合、反距离约束等）来扩展协议，以加快程序并改进云派生结构。该项目的主要目标是开发强大的计算协议，以使用最少量的 NMR 实验数据获得蛋白质折叠。