Perrakis A, Morris R, Lamzin V S
European Molecular Biology Laboratory (EMBL), Grenoble Outstation, France.
Nat Struct Biol. 1999 May;6(5):458-63. doi: 10.1038/8263.
In protein crystallography, much time and effort are often required to trace an initial model from an interpretable electron density map and to refine it until it best agrees with the crystallographic data. Here, we present a method to build and refine a protein model automatically and without user intervention, starting from diffraction data extending to resolution higher than 2.3 A and reasonable estimates of crystallographic phases. The method is based on an iterative procedure that describes the electron density map as a set of unconnected atoms and then searches for protein-like patterns. Automatic pattern recognition (model building) combined with refinement, allows a structural model to be obtained reliably within a few CPU hours. We demonstrate the power of the method with examples of a few recently solved structures.
在蛋白质晶体学中,通常需要花费大量时间和精力从可解释的电子密度图追踪初始模型,并对其进行优化,直到它与晶体学数据达到最佳契合。在此,我们提出一种方法,可从分辨率高于2.3埃的衍射数据和晶体学相位的合理估计值出发,自动且无需用户干预地构建和优化蛋白质模型。该方法基于一个迭代过程,将电子密度图描述为一组不相连的原子,然后搜索类似蛋白质的模式。自动模式识别(模型构建)与优化相结合,能够在几个中央处理器小时内可靠地获得结构模型。我们通过几个最近解析的结构示例展示了该方法的强大功能。