Fast and accurate fitting of relaxation dispersion data using the flexible software package GLOVE.
Fast and accurate fitting of relaxation dispersion data using the flexible software package GLOVE.
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DOI：
10.1007/s10858-013-9747-5
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发表时间：
2013-07
影响因子：
2.7
通讯作者：
Wright PE
中科院分区：
文献类型：
--
作者：
Sugase K;Konuma T;Lansing JC;Wright PE
弛豫色散光谱是蛋白质动力学分析中应用最广泛的技术之一。为了从动力学的角度详细了解蛋白质的功能，必须准确地拟合弛豫色散数据。网格搜索法通常用于松弛色散曲线拟合，但它并不总是能找到提供最佳拟合参数集的全局最小值。同时，随着网格尺寸的增大，计算时间的增加，拟合质量并不一定提高。这是因为松弛色散曲线拟合存在一个局部最小值问题，这是非线性最小二乘曲线拟合中的一个普遍问题。因此，为了快速准确地拟合松弛色散数据，我们开发了一种新的拟合程序GLOVE，该程序交替最小化全局和局部参数，并结合蒙特卡罗最小化方法，使拟合参数通过局部极小值，计算成本低。GLOVE还实现了一个随机搜索方法，该方法在用户定义的范围内随机设置初始参数值。我们在这里证明了三种方法的联合使用可以比单独的网格搜索更快、更准确地找到全局最小值。
Relaxation dispersion spectroscopy is one of the most widely used techniques for the analysis of protein dynamics. To obtain a detailed understanding of the protein function from the view point of dynamics, it is essential to fit relaxation dispersion data accurately. The grid search method is commonly used for relaxation dispersion curve fits, but it does not always find the global minimum that provides the best-fit parameter set. Also, the fitting quality does not always improve with increase of the grid size although the computational time becomes longer. This is because relaxation dispersion curve fitting suffers from a local minimum problem, which is a general problem in non-linear least squares curve fitting. Therefore, in order to fit relaxation dispersion data rapidly and accurately, we developed a new fitting program called GLOVE that minimizes global and local parameters alternately, and incorporates a Monte-Carlo minimization method that enables fitting parameters to pass through local minima with low computational cost. GLOVE also implements a random search method, which sets up initial parameter values randomly within user-defined ranges. We demonstrate here that the combined use of the three methods can find the global minimum more rapidly and more accurately than grid search alone.
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影响因子：
5.6
作者：
Schanda, Paul;Brutscher, Bernhard;Tollinger, Martin
通讯作者：
Tollinger, Martin
DOI：
10.1126/science.1198542
发表时间：
2011-04-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bhabha G;Lee J;Ekiert DC;Gam J;Wilson IA;Dyson HJ;Benkovic SJ;Wright PE
通讯作者：
Wright PE
影响因子：
64.8
作者：
Bouvignies, Guillaume;Vallurupalli, Pramodh;Hansen, D. Flemming;Correia, Bruno E.;Lange, Oliver;Bah, Alaji;Vernon, Robert M.;Dahlquist, Frederick W.;Baker, David;Kay, Lewis E.
通讯作者：
Kay, Lewis E.
影响因子：
2.2
作者：
CARVER, JP;RICHARDS, RE
通讯作者：
RICHARDS, RE
DOI：
10.1073/pnas.0804221105
发表时间：
2008-08-19
影响因子：
11.1
作者：
Vallurupalli, Pramodh;Hansen, D. Flemming;Kay, Lewis E.
通讯作者：
Kay, Lewis E.