Data mining the BMRB/PDB for correlations useful for NMR

产品说明： 使用NMR光谱法求解生物大分子的三维结构对于开发几种当前使用的治疗药物是有用的，并且可能在许多未来药物的开发中发挥关键作用。通过这种方法的结构测定基本上是一个三步过程，需要共振分配（确定分子中的哪个核产生哪个NMR信号），从NMR参数（如原子间距离和扭转角约束）推导结构约束和数据的结构建模。众所周知，最终结构的精度和实用性随着结构计算中使用的约束数量的增加而增加。由于人工分析时间和光谱仪时间都是有限的资源，因此最大限度地提高共振分配和约束确定的数据收集和分析效率至关重要。经验确定的NMR观测值和结构参数之间的相关性的使用已经至关重要的NMR结构测定的进展和新发现的相关性肯定会产生类似的进展。 NMR和X射线结构目前正在以每年约5000的速度进行测定。 蛋白质数据库（PDB）现在包含超过27000个结构的坐标，BioMagResBank（BMRB）包含超过3000个大分子的NMR参数。这些数据库为发现分子结构特征与反映它们的NMR参数之间的新相关性提供了巨大的资源。虽然PDB和BRMB的架构对于将这些数据存档和检索到全球科学界来说是非凡的，但公共数据库不提供挖掘这种数据的一致性以用于NMR结构确定的相关性的能力。这种能力对于充分利用大量可用的后基因组数据至关重要。我们建议开发这样的资源，并使用它来挖掘隐藏在数据中的重要相关性，这些数据有助于提高NMR分析的效率和有效性。

DESCRIPTION: Solving the three-dimensional structure of biological macromolecules using NMR spectroscopy has been useful for the development of several currently used therapeutic drugs and will likely play a key role in the development of many future drugs. Structure determination by this approach is essentially a three-step process requiring resonance assignment (determining which nucleus in the molecule gives rise to which NMR signal), derivation of structural restraints from NMR parameters (such as interatomic distance and torsion angle constraints) and structural modeling of the data. It is known that the precision and usefulness of the final structure increases with the number of restraints used in the structure calculation. As both human analysis time and spectrometer time are limiting resources, it is critical to maximize the efficiency of data collection and analysis for both resonance assignment and restraint determination. The use of empirically determined correlations between NMR observables and structural parameters has been vital to the progress of NMR structure determination and newly discovered correlations are certainly expected to generate similar advances. NMR and x-ray structures are currently being determined at a rate of approximately 5000 per year. The Protein Databank (PDB) now houses the coordinates of over 27000 structures and the BioMagResBank (BMRB) contains NMR parameters for over 3000 macromolecules. These databanks provide an enormous resource for discovering novel correlations between the structural features of molecules and the NMR parameters which reflect them. While the architectures of the PDB and BRMB are extraordinary for the archival and retrieval of these data to the global scientific community, the public databanks do not offer the capability for mining this enormity of data for correlations useful to NMR structure determination. This capability is essential in order to fully exploit the vast amounts of available postgenomic data. We propose to develop such a resource and use it to mine for important correlations hidden within the data which are useful in improving the efficiency and effectiveness of NMR analysis.