Data mining the BMRB/PDB for correlations useful for NMR

说明： 利用核磁共振波谱技术解决生物大分子的三维结构问题，对于开发目前正在使用的几种治疗药物是有用的，并可能在未来许多药物的开发中发挥关键作用。用这种方法确定结构本质上是一个三步过程，需要共振指定(确定分子中的哪个核产生哪个核磁共振信号)，从核磁共振参数推导结构约束(如原子间距离和扭角约束)，以及数据的结构建模。众所周知，随着结构计算中使用的约束数目的增加，最终结构的精度和实用性都会增加。由于人类分析时间和光谱仪时间都是有限的资源，最大限度地提高数据收集和分析的效率对于共振分配和约束确定至关重要。利用核磁共振可观测值与结构参数之间的经验确定的相关性对核磁共振结构确定的进展至关重要，新发现的相关性肯定会产生类似的进展。 目前正以每年大约5000个的速度测定核磁共振和X射线结构。 蛋白质数据库现在包含了27000多个结构的坐标，生物磁共振库包含了3,000多个大分子的核磁共振参数。这些数据库为发现分子结构特征和反映它们的核磁共振参数之间的新关联提供了巨大的资源。虽然PDB和B人民币的体系结构对于全球科学界存档和检索这些数据来说是非同寻常的，但公共数据库没有提供挖掘这些海量数据的能力，以寻找对核磁共振结构确定有用的关联。为了充分利用大量的后基因组数据，这种能力是必不可少的。我们建议开发这样的资源，并将其用于挖掘隐藏在数据中的重要关联，这些关联有助于提高核磁共振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.