Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB-CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain.
Department of Structural Chemistry, Georg August University of Göttingen, Tammannstrasse 4, 37077 Göttingen, Germany.
Acta Crystallogr D Struct Biol. 2018 Apr 1;74(Pt 4):290-304. doi: 10.1107/S2059798318001365. Epub 2018 Apr 3.
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 Å 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.
大分子结构可以通过分子置换来解决,只要有合适的搜索模型即可。来自远缘同源物的模型可能与目标结构相差太大而无法成功,尽管它们具有相似的整体折叠甚至是非常接近的特征区域。成功地利用这些模板的方法通常依赖于保守性程度来选择和改进搜索模型。ARCIMBOLDO_SHREDDER 使用来自远缘同源物的片段,采用基于实验数据的强制方法,而不是基于序列相似性。ARCIMBOLDO_SHREDDER 中实现的新算法被详细描述,通过解决新结构和测试结构来阐明其特征方面。与以前基于省略或提取连续多肽跨度的算法相比,该算法的改进是现在使用三维体积来尊重结构单元。最佳片段大小是根据预期的对数似然增益 (LLG) 值来估计的,这些值是假设可以找到一个具有接近成功扩展结构所需的准确性的亚结构,通常低于目标的 0.6 Å 均方根偏差 (r.m.s.d.)。通过模型修剪或刚性组分解以及通过 Phaser 的回旋细化进行优化来尝试更好的采样。此外,在模型转换、包装过滤和细化之后,模型要么被分解为预定的刚性组并进行细化( gimble 细化),要么使用 Phaser 的 LLG 引导修剪来修剪模型中在目标 r.m.s.d. 值下不贡献信号的残基。在 ALIXE 中在倒空间中执行一致的部分解决方案的相位组合。最后,在 SHELXE 中进行密度修正和主链自动跟踪,以扩展到完整结构并识别成功的解决方案。描述了对测试数据的性能和新结构的解决方案。