Automated refinement of macromolecular structures at low resolution using prior information.
Automated refinement of macromolecular structures at low resolution using prior information.
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DOI：
10.1107/s2059798316014534
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发表时间：
2016-10-01
期刊：
影响因子：
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通讯作者：
Murshudov GN
中科院分区：
文献类型：
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作者：
Kovalevskiy O;Nicholls RA;Murshudov GN
已经开发了一种用于低分辨率结构细化的自动化管道(LORESTR)，以帮助轻松地对疑难病例进行细化。该管道自动选择用于外部约束生成的高分辨率同系物，并优化ProSMART和REFMAC5的参数，在94%的测试用例中改善R因子和几何统计。由于观测次数与可调参数的比率在低分辨率时很小，因此有必要使用补充信息来分析这类数据。ProSMART是一个程序，它可以使用同源结构为大分子生成约束，以及为二级结构的稳定生成通用约束。REFMAC5使用这些约束来稳定原子模型的精化。然而，最优的精化方案因情况而异，如何选择合适的同源结构(S)或其他先验信息来源用于约束生成并不总是显而易见的。在对低分辨率模型的大量数据集进行了广泛的测试后，已经确定了用于选择同源结构的性能最好的精化协议和策略。这些策略和协议已在低分辨率结构细化(LORESTR)流水线中实现。该管道执行孪生的自动检测，并选择最佳的结垢方法和溶剂参数。LORESTR可以使用用户提供的同源结构，也可以运行自动BLAST搜索并从PDB下载同源。管道使用不同的参数执行多个模型细化实例，以找到最佳协议。测试表明，自动流水线改善了测试集中PDB中94%的低分辨率案例的R因子、几何形状和Ramachandran统计量。
An automated pipeline for low-resolution structure refinement (LORESTR) has been developed to assist in the hassle-free refinement of difficult cases. The pipeline automates the selection of high-resolution homologues for external restraint generation and optimizes the parameters for ProSMART and REFMAC5, improving R factors and geometry statistics in 94% of the test cases. Since the ratio of the number of observations to adjustable parameters is small at low resolution, it is necessary to use complementary information for the analysis of such data. ProSMART is a program that can generate restraints for macromolecules using homologous structures, as well as generic restraints for the stabilization of secondary structures. These restraints are used by REFMAC5 to stabilize the refinement of an atomic model. However, the optimal refinement protocol varies from case to case, and it is not always obvious how to select appropriate homologous structure(s), or other sources of prior information, for restraint generation. After running extensive tests on a large data set of low-resolution models, the best-performing refinement protocols and strategies for the selection of homologous structures have been identified. These strategies and protocols have been implemented in the Low-Resolution Structure Refinement (LORESTR) pipeline. The pipeline performs auto-detection of twinning and selects the optimal scaling method and solvent parameters. LORESTR can either use user-supplied homologous structures, or run an automated BLAST search and download homologues from the PDB. The pipeline executes multiple model-refinement instances using different parameters in order to find the best protocol. Tests show that the automated pipeline improves R factors, geometry and Ramachandran statistics for 94% of the low-resolution cases from the PDB included in the test set.