Combining phase information in reciprocal space for molecular replacement with partial models.

Claudia Millán, Massimo Sammito, Irene Garcia-Ferrer, Theodoros Goulas, George M Sheldrick, Isabel Usón
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引用次数: 17

Abstract

ARCIMBOLDO allows ab initio phasing of macromolecular structures below atomic resolution by exploiting the location of small model fragments combined with density modification in a multisolution frame. The model fragments can be either secondary-structure elements predicted from the sequence or tertiary-structure fragments. The latter can be derived from libraries of typical local folds or from related structures, such as a low-homology model that is unsuccessful in molecular replacement. In all ARCIMBOLDO applications, fragments are searched for sequentially. Correct partial solutions obtained after each fragment-search stage but lacking the necessary phasing power can, if combined, succeed. Here, an analysis is presented of the clustering of partial solutions in reciprocal space and of its application to a set of different cases. In practice, the task of combining model fragments from an ARCIMBOLDO run requires their referral to a common origin and is complicated by the presence of correct and incorrect solutions as well as by their not being independent. The F-weighted mean phase difference has been used as a figure of merit. Clustering perfect, non-overlapping fragments dismembered from test structures in polar and nonpolar space groups shows that density modification before determining the relative origin shift enhances its discrimination. In the case of nonpolar space groups, clustering of ARCIMBOLDO solutions from secondary-structure models is feasible. The use of partially overlapping search fragments provides a more favourable circumstance and was assessed on a test case. Applying the devised strategy, a previously unknown structure was solved from clustered correct partial solutions.

结合互易空间相信息与部分模型进行分子置换。
ARCIMBOLDO通过利用小模型片段的位置,结合多溶液框架中的密度修改,允许在原子分辨率以下从头开始分相大分子结构。模型片段既可以是由序列预测的二级结构片段,也可以是三级结构片段。后者可以从典型的局部折叠库或相关结构中获得,例如在分子替换中不成功的低同源性模型。在所有ARCIMBOLDO应用程序中,片段都是按顺序搜索的。在每个片段搜索阶段得到正确的部分解,但缺乏必要的相位功率,如果组合,可以成功。本文分析了倒易空间中部分解的聚类及其在一系列不同情况下的应用。在实践中,从ARCIMBOLDO运行中组合模型片段的任务需要将它们引用到一个共同的起源,并且由于正确和不正确的解决方案的存在以及它们不是独立的而变得复杂。采用f加权平均相位差作为优值。从极性和非极性空间群中分离出的测试结构碎片聚类完美且不重叠,表明在确定相对原点位移之前进行密度修正可以增强其识别能力。在非极性空间群的情况下,二级结构模型的ARCIMBOLDO解聚类是可行的。使用部分重叠的搜索片段提供了一个更有利的环境,并在一个测试用例中进行了评估。应用所设计的策略,从聚类的正确部分解中求解出先前未知的结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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