Suppr超能文献

处理涉及生物大分子结构计算中等效或未立体分配质子的 NOE 约束。

Treatment of NOE constraints involving equivalent or nonstereoassigned protons in calculations of biomacromolecular structures.

机构信息

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 02115, Boston, MA, USA.

出版信息

J Biomol NMR. 1996 Oct;8(3):292-310. doi: 10.1007/BF00410328.

Abstract

Two modifications to the commonly used protocols for calculating NMR structures are developed, relating to the treatment of NOE constraints involving groups of equivalent protons or nonstereoassigned diastereotopic protons. Firstly, a modified method is investigated for correcting for multiplicity, which is applicable whenever all NOE intensities are calibrated as a single set and categorised in broad intensity ranges. Secondly, a new set of values for 'pseudoatom corrections' is proposed for use with calculations employing 'centre-averaging'. The effect of these protocols on structure calculations is demonstrated using two proteins, one of which is well defined by the NOE data, the other less so. It is shown that failure to correct for multiplicity when using 'r(-6) averaging' results in overly precise structures, higher NOE energies and deviations from geometric ideality, while failure to correct for multiplicity when using 'r(-6) summation' can cause an avoidable degradation of precision if the NOE data are sparse. Conversely, when multiplicities are treated correctly, r(-6) averaging, r(-6) summation and centre averaging all give closely comparable results when the structure is well defined by the data. When the NOE data contain less information, r(-6) averaging or r(-6) summation offer a significant advantage over centre averaging, both in terms of precision and in terms of the proportion of calculations that converge on a consisten result.

摘要

开发了两种常用的 NMR 结构计算方法的修改,涉及处理涉及等效质子或非立体非对映质子组的 NOE 约束。首先,研究了一种改进的方法来校正多重性,只要所有的 NOE 强度都作为一组校准,并在广泛的强度范围内进行分类,就可以应用这种方法。其次,提出了一组新的“伪原子校正”值,用于与采用“中心平均”的计算相结合。使用两种蛋白质来证明这些方案对结构计算的影响,其中一种蛋白质由 NOE 数据很好地定义,另一种蛋白质则不太好。结果表明,在使用“r(-6)平均”时,如果不校正多重性,会导致结构过于精确,NOE 能量更高,偏离几何理想性,而在使用“r(-6)求和”时,如果不校正多重性,并且如果 NOE 数据稀疏,则可能导致不必要的精度降低。相反,当正确处理多重性时,当数据很好地定义结构时,r(-6)平均、r(-6)求和和中心平均都会给出非常接近的结果。当 NOE 数据包含较少的信息时,r(-6)平均或 r(-6)求和比中心平均具有显著优势,无论是在精度方面还是在能够收敛到一致结果的计算比例方面。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验