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首页> 外文期刊>Acta crystallographica.Section D. Biological crystallography >Error-estimation-guided rebuilding of de novo models increases the success rate of ab initio phasing
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Error-estimation-guided rebuilding of de novo models increases the success rate of ab initio phasing

机译：Error-estimation-guided新创的重建模型增加了从头开始的成功率定相

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摘要

Recent advancements in computational methods for protein-structure prediction have made it possible to generate the high-quality de novo models required for ab initio phasing of crystallographic diffraction data using molecular replacement. Despite those encouraging achievements in ab initio phasing using de novo models, its success is limited only to those targets for which high-quality de novo models can be generated. In order to increase the scope of targets to which ab initio phasing with de novo models can be successfully applied, it is necessary to reduce the errors in the de novo models that are used as templates for molecular replacement. Here, an approach is introduced that can identify and rebuild the residues with larger errors, which subsequently reduces the overall C root-mean-square deviation (CA-RMSD) from the native protein structure. The error in a predicted model is estimated from the average pairwise geometric distance per residue computed among selected lowest energy coarse-grained models. This score is subsequently employed to guide a rebuilding process that focuses on more error-prone residues in the coarse-grained models. This rebuilding methodology has been tested on ten protein targets that were unsuccessful using previous methods. The average CA-RMSD of the coarse-grained models was improved from 4.93 to 4.06 ?. For those models with CA-RMSD less than 3.0 ?, the average CA-RMSD was improved from 3.38 to 2.60 ?. These rebuilt coarse-grained models were then converted into all-atom models and refined to produce improved de novo models for molecular replacement. Seven diffraction data sets were successfully phased using rebuilt de novo models, indicating the improved quality of these rebuilt de novo models and the effectiveness of the rebuilding process. Software implementing this method, called MORPHEUS, can be downloaded from http://www.riken.jp/zhangiru/software.html.
机译：最近的进步计算方法预测工作了可以生成高质量的新创模型需要从头开始逐步的用分子晶体衍射数据更换。成就从头开始逐步使用新创模型,它的成功是有限的只有这些目标的高质量的新创模型生成。目标,从头开始逐步与新创模型可以成功地应用,它是必要的新创中减少错误模型作为模板分子更换。可以识别和重建较大的残留错误,随后降低了整体C均方根偏差(CA-RMSD)本机蛋白质结构。预测模型估计的平均水平成对的几何距离计算每残渣在选定的最低能量粗粒度模型。指导一个重建的过程,关注更多容易出错的粗粒度的残留物模型。十蛋白质测试目标使用之前的方法失败。CA-RMSD粗粒度模型的改进从4.93到4.06 ?。CA-RMSD小于3.0 ?,平均CA-RMSD从3.38提高到2.60 ?。然后转化为粗粒度模型所有原子模型和精制生产改善新创模型分子置换。衍射数据集被成功分阶段使用新创模型重建,指示改善这些新创重建模型的质量和重建过程的有效性。软件实现该方法,称为睡眠,可以从下载http://www.riken.jp/zhangiru/software.html。

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