Refinement of severely incomplete structures with maximum likelihood in BUSTER-TNT
Refinement of severely incomplete structures with maximum likelihood in BUSTER-TNT
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DOI：
10.1107/s0907444904016427
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发表时间：
2004-12-01
影响因子：
2.2
通讯作者：
Bricogne, G
中科院分区：
文献类型：
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作者：
Blanc, E;Roversi, P;Bricogne, G
BUSTER - TNT是一种最大可能性的大分子精炼包。BUSTER组装结构模型，缩放观察和计算的结构因子幅度，并计算模型似然性，而TNT处理立体化学和NCS约束/约束，并移动原子坐标，B因子和占位符。在真实的空间中，除了传统的原子模型和本体溶剂模型之外，BUSTER还将原子模型尚不可用的结构部分（“缺失结构”）建模为缺失原子随机位置的低分辨率概率分布。在倒易空间中，复平面中的BUSTER结构因子分布是以原子、本体溶剂和缺失结构模型计算的结构因子为中心的二维高斯分布。将与这三个结构分量相关联的误差相加，以计算高斯分布的总体分布。当原子模型非常不完整时，缺失结构的建模和BUSTER统计模型的一致性有助于结构的建立和完成，因为（i）增加了总体比例因子的准确性，（ii）通过考虑来自缺失结构的一些散射来减少影响原子模型细化的偏差，（iii）将空间定义添加到不完整性的来源改进了传统的基于Luzzati和sigma（A）的误差模型，以及（iv）该程序可以单独在未构建结构的区域中执行选择性密度修改。
BUSTER - TNT is a maximum-likelihood macromolecular refinement package. BUSTER assembles the structural model, scales observed and calculated structure-factor amplitudes and computes the model likelihood, whilst TNT handles the stereochemistry and NCS restraints/constraints and shifts the atomic coordinates, B factors and occupancies. In real space, in addition to the traditional atomic and bulk-solvent models, BUSTER models the parts of the structure for which an atomic model is not yet available ('missing structure') as low-resolution probability distributions for the random positions of the missing atoms. In reciprocal space, the BUSTER structure-factor distribution in the complex plane is a two-dimensional Gaussian centred around the structure factor calculated from the atomic, bulk-solvent and missing-structure models. The errors associated with these three structural components are added to compute the overall spread of the Gaussian. When the atomic model is very incomplete, modelling of the missing structure and the consistency of the BUSTER statistical model help structure building and completion because ( i) the accuracy of the overall scale factors is increased, (ii) the bias affecting atomic model refinement is reduced by accounting for some of the scattering from the missing structure, (iii) the addition of a spatial definition to the source of incompleteness improves on traditional Luzzati and sigma(A)-based error models and (iv) the program can perform selective density modification in the regions of unbuilt structure alone.