Prospects for de novo phasing with de novo protein models.
Prospects for de novo phasing with de novo protein models.
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DOI：
10.1107/s0907444908020039
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发表时间：
2009-02
期刊：
影响因子：
--
通讯作者：
Baker, David
中科院分区：
文献类型：
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作者：
Das, Rhiju;Baker, David
在首次系统探索Rosetta de novo模型的相位中，研究表明，粗粒度模型的全原子细化显著提高了使用Phaser软件进行分子替换时的模型质量和性能。“从头开始”相位衍射数据集的前景是吸引人的，但在很大程度上尚未经过测试。在首次系统探索Rosetta de novo模型的相位中，研究表明，粗粒度模型的全原子细化显著提高了使用Phaser软件进行分子替换时的模型质量和性能。提出了15个新的衍射数据集，这些数据集明确地与新模型相结合。这些衍射数据集代表了9个空间群，涵盖了很大范围的溶剂含量（33-79%）和不对称单位拷贝数（1-4）。在溶剂含量或不对称单位拷贝数之间没有观察到相化的容易程度。相反，与模型蛋白质的长度存在微弱的相关性：较大的蛋白质需要稍微不那么精确的模型来成功地进行分子替换。总的来说，这项调查的结果表明，从头模型可以对大约六分之一的大小为100个残基或更少的蛋白质进行相衍射数据。然而，在许多情况下，“用新模型进行新阶段”需要大量的计算能力投入，每个目标的CPU使用时间远远超过103天。如果用从头开始的分子替换模型要成为一种实用的工具来寻找与先前解决的蛋白质结构没有同源性的靶标，那么改进构象搜索方法是必要的。
In a first systematic exploration of phasing with Rosetta de novo models, it is shown that all-atom refinement of coarse-grained models significantly improves both the model quality and performance in molecular replacement with the Phaser software. The prospect of phasing diffraction data sets ‘de novo’ for proteins with previously unseen folds is appealing but largely untested. In a first systematic exploration of phasing with Rosetta de novo models, it is shown that all-atom refinement of coarse-grained models significantly improves both the model quality and performance in molecular replacement with the Phaser software. 15 new cases of diffraction data sets that are unambiguously phased with de novo models are presented. These diffraction data sets represent nine space groups and span a large range of solvent contents (33–79%) and asymmetric unit copy numbers (1–4). No correlation is observed between the ease of phasing and the solvent content or asymmetric unit copy number. Instead, a weak correlation is found with the length of the modeled protein: larger proteins required somewhat less accurate models to give successful molecular replacement. Overall, the results of this survey suggest that de novo models can phase diffraction data for approximately one sixth of proteins with sizes of 100 residues or less. However, for many of these cases, ‘de novo phasing with de novo models’ requires significant investment of computational power, much greater than 103 CPU days per target. Improvements in conformational search methods will be necessary if molecular replacement with de novo models is to become a practical tool for targets without homology to previously solved protein structures.