Habeck Michael, Rieping Wolfgang, Nilges Michael
Unité de Bioinformatique Structurale, Institut Pasteur, Centre National de la Recherche Scientifique Unité de Recherche Associée 2185, 25-28, Rue du Dr Roux, 75724 Paris Cedex 15, France.
Proc Natl Acad Sci U S A. 2006 Feb 7;103(6):1756-61. doi: 10.1073/pnas.0506412103. Epub 2006 Jan 30.
The determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information. Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality. Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation.
大分子结构的确定需要根据先前的物理信息对实验证据进行加权。尽管实验数据会严重影响计算结构的质量，但通常是基于经验对其进行加权。目前，交叉验证是确定最佳权重最严格的方法。我们描述了一种在结构计算过程中自适应加权实验数据的通用方法。进一步表明，可以完全消除为数据定义权重的必要性。我们通过一个基于核磁共振（NMR）数据的结构计算来演示该方法，发现所得结构在准确性和结构质量方面是最优的。我们的方法没有因权重的经验性选择而产生的偏差，并且在通过交叉验证估计权重方面具有一些优势。
