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首页> 外文期刊>Acta Crystallographica, Section B. Structural Science >Structure Solution by Iterative Peaklist Optimization and Tangent Expansion in Space Group PI
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Structure Solution by Iterative Peaklist Optimization and Tangent Expansion in Space Group PI

机译:空间群PI中迭代峰表优化和切线展开的结构解

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摘要

An extension to the peaklist optimization procedure is proposed, in which one overall phase refinement cycle consists of tangent expansion, £-map, peaksearch and elimination of peaks to achieve a maximum correlation coefficient between Eo and Ec. This procedure appears to be able to solve large structures from random phases given data to atomic resolution. The power of the method can be substantially increased by starting with slightly better than random phases, obtained for example from threefold Patterson vector superposition minimum functions or rotation searches using a fragment of known geometry. These two sources of phase information require expansion of the data to the space group PI, which also appears to be a useful strategy when starting from random phases. This real/reciprocal space recycling procedure was successful in solving two small known proteins and three unknown 200+-atom small-molecule structures. An investigation of the influence of the resolution on the peaklist optimization algorithm shows that there is a marked deterioration in the effectiveness as the resolution of the data is truncated, the deterioration being particularly marked between 1.2 and 1.3 A.
机译:提出了对峰列表优化程序的扩展,其中一个整体相位优化周期包括切线扩展,α-图,峰搜索和峰消除,以实现Eo和Ec之间的最大相关系数。该程序似乎能够解决从给定数据到原子分辨率的随机相位的大型结构。该方法的功能可以通过从略好于随机相位的位置开始显着提高,例如从三重Patterson矢量叠加最小函数或使用已知几何形状的片段进行旋转搜索获得的随机相位开始。这两个相位信息源需要将数据扩展到空间组PI,当从随机相位开始时,这似乎也是一种有用的策略。这种真实/对等的空间回收程序成功地解决了两个已知的小蛋白质和三个未知的200+原子的小分子结构。对分辨率对峰列表优化算法的影响的研究表明,随着数据分辨率的缩短,有效性显着下降,尤其是在1.2和1.3 A之间显着下降。

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