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首页> 外文期刊>Acta crystallographica. Section D, Structural biology. >Exploiting distant homologues for phasing through the generation of compact fragments, local fold refinement and partial solution combination
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Exploiting distant homologues for phasing through the generation of compact fragments, local fold refinement and partial solution combination

机译：利用逐步通过遥远的同系物代紧凑片段,当地的褶皱精化和部分解决方案组合

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Macromolecular structures can be solved by molecular replacement provided that suitable search models are available. Models from distant homologues may deviate too much from the target structure to succeed, notwithstanding an overall similar fold or even their featuring areas of very close geometry. Successful methods to make the most of such templates usually rely on the degree of conservation to select and improve search models. ARCIMBOLDO_SHREDDER uses fragments derived from distant homologues in a brute‐force approach driven by the experimental data, instead of by sequence similarity. The new algorithms implemented in ARCIMBOLDO_SHREDDER are described in detail, illustrating its characteristic aspects in the solution of new and test structures. In an advance from the previously published algorithm, which was based on omitting or extracting contiguous polypeptide spans, model generation now uses three‐dimensional volumes respecting structural units. The optimal fragment size is estimated from the expected log‐likelihood gain (LLG) values computed assuming that a substructure can be found with a level of accuracy near that required for successful extension of the structure, typically below 0.6?? root‐mean‐square deviation (r.m.s.d.) from the target. Better sampling is attempted through model trimming or decomposition into rigid groups and optimization through Phaser 's gyre refinement. Also, after model translation, packing filtering and refinement, models are either disassembled into predetermined rigid groups and refined ( gimble refinement) or Phaser 's LLG‐guided pruning is used to trim the model of residues that are not contributing signal to the LLG at the target r.m.s.d. value. Phase combination among consistent partial solutions is performed in reciprocal space with ALIXE . Finally, density modification and main‐chain autotracing in SHELXE serve to expand to the full structure and identify successful solutions. The performance on test data and the solution of new structures are described.
机译：大分子结构可以解决分子置换提供合适搜索模型是可用的。从目标同系物可能偏离太多结构要取得成功,尽管一个整体相似褶皱,甚至他们的领域非常接近几何。最通常依赖于这样的模板程度的保护选择和改进搜索模型。来自遥远的同系物的蛮力由实验数据的方法,来代替的序列相似性。ARCIMBOLDO_SHREDDER中实现详细说明其特点新和测试方面的解决方案结构。发表的算法,它是基于省略或提取的多肽跨越,模型代现在使用三维卷尊重的结构单位。从预期的大小估计日志量获得(LLG)值计算的可能性假设一个子结构可以找到水平所需的精度接近成功的扩展的结构,通常低于0.6 ? ?从目标。通过削减或分解成模型严格的组织,通过移相器的优化环流提纯。包装筛选和优化模型可以拆卸成预定的刚性组和精制(平衡台细化)或移相器的LLG引导修剪用于削减模型不造成信号的残留物在目标r.m.s.d. LLG值。组合中一致的部分解决方案执行与ALIXE互惠空间。最后,修改密度和主链autotrace在SHELXE扩大结构和识别成功的解决方案。性能测试数据和新的解决方案结构描述。

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