Gorba Christian, Miyashita Osamu, Tama Florence
Department of Biochemistry and Molecular Biophysics, The University of Arizona, Tucson, Arizona, USA.
Biophys J. 2008 Mar 1;94(5):1589-99. doi: 10.1529/biophysj.107.122218. Epub 2007 Nov 9.
We present a method for reconstructing a 3D structure from a pair distribution function by flexibly fitting known x-ray structures toward a conformation that agrees with the low-resolution data. This method uses a linear combination of low-frequency normal modes from elastic-network description of the molecule in an iterative manner to deform the structure optimally to conform to the target pair distribution function. A simple function, pair distance distribution function between atoms, is chosen as a test model to establish computational algorithms-optimization algorithm and scoring function-that can utilize low-resolution 1D data. To select a correct structural model based on less information, we developed a scoring function that takes into account a characteristic of pair distribution functions. In addition, we employ a new optimization algorithm, the trusted region method, that relies on both first and second derivatives of the scoring function. Illustrative results of our studies on simulated 1D data from five different proteins, for which large conformational changes are known to occur, are presented.
我们提出了一种通过将已知的X射线结构灵活拟合至与低分辨率数据相符的构象，从对分布函数重建三维结构的方法。该方法以迭代方式使用来自分子弹性网络描述的低频正常模式的线性组合，对结构进行最佳变形，以符合目标对分布函数。选择一个简单的函数，即原子间的对距离分布函数，作为测试模型来建立可利用低分辨率一维数据的计算算法——优化算法和评分函数。为了基于较少的信息选择正确的结构模型，我们开发了一种考虑对分布函数特征的评分函数。此外，我们采用了一种新的优化算法——信赖域方法，该方法依赖于评分函数的一阶和二阶导数。本文展示了我们对五种不同蛋白质的模拟一维数据的研究结果，已知这些蛋白质会发生大的构象变化。
