d'Auvergne Edward J, Gooley Paul R
Department of NMR-based Structural Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, D-37077, Goettingen, Germany.
J Biomol NMR. 2008 Feb;40(2):107-19. doi: 10.1007/s10858-007-9214-2. Epub 2007 Dec 18.
The key to obtaining the model-free description of the dynamics of a macromolecule is the optimisation of the model-free and Brownian rotational diffusion parameters using the collected R (1), R (2) and steady-state NOE relaxation data. The problem of optimising the chi-squared value is often assumed to be trivial, however, the long chain of dependencies required for its calculation complicates the model-free chi-squared space. Convolutions are induced by the Lorentzian form of the spectral density functions, the linear recombinations of certain spectral density values to obtain the relaxation rates, the calculation of the NOE using the ratio of two of these rates, and finally the quadratic form of the chi-squared equation itself. Two major topological features of the model-free space complicate optimisation. The first is a long, shallow valley which commences at infinite correlation times and gradually approaches the minimum. The most severe convolution occurs for motions on two timescales in which the minimum is often located at the end of a long, deep, curved tunnel or multidimensional valley through the space. A large number of optimisation algorithms will be investigated and their performance compared to determine which techniques are suitable for use in model-free analysis. Local optimisation algorithms will be shown to be sufficient for minimisation not only within the model-free space but also for the minimisation of the Brownian rotational diffusion tensor. In addition the performance of the programs Modelfree and Dasha are investigated. A number of model-free optimisation failures were identified: the inability to slide along the limits, the singular matrix failure of the Levenberg-Marquardt minimisation algorithm, the low precision of both programs, and a bug in Modelfree. Significantly, the singular matrix failure of the Levenberg-Marquardt algorithm occurs when internal correlation times are undefined and is greatly amplified in model-free analysis by both the grid search and constraint algorithms. The program relax ( http://www.nmr-relax.com ) is also presented as a new software package designed for the analysis of macromolecular dynamics through the use of NMR relaxation data and which alleviates all of the problems inherent within model-free analysis.
获得大分子动力学无模型描述的关键在于利用收集到的R(1)、R(2)和稳态NOE弛豫数据优化无模型和布朗旋转扩散参数。通常认为优化卡方值的问题很简单,然而,其计算所需的长依赖链使无模型卡方空间变得复杂。卷积由谱密度函数的洛伦兹形式、某些谱密度值的线性重组以获得弛豫率、使用其中两个率的比值计算NOE以及最后卡方方程本身的二次形式引起。无模型空间的两个主要拓扑特征使优化变得复杂。第一个是一个长而浅的谷,它从无限相关时间开始,逐渐接近最小值。最严重的卷积发生在两个时间尺度的运动中,其中最小值通常位于穿过该空间的长而深的弯曲隧道或多维谷的末端。将研究大量优化算法并比较它们的性能,以确定哪些技术适用于无模型分析。局部优化算法不仅在无模型空间内进行最小化时足够,而且对于布朗旋转扩散张量的最小化也足够。此外,还研究了程序Modelfree和Dasha的性能。确定了一些无模型优化失败的情况:无法沿极限滑动、Levenberg-Marquardt最小化算法的奇异矩阵失败、两个程序的低精度以及Modelfree中的一个错误。值得注意的是,当内部相关时间未定义时,Levenberg-Marquardt算法会出现奇异矩阵失败,并且在无模型分析中通过网格搜索和约束算法会大大放大这种失败。还介绍了程序relax(http://www.nmr-relax.com),它是一个新的软件包,设计用于通过使用NMR弛豫数据分析大分子动力学,并且缓解了无模型分析中固有的所有问题。