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Optimisation of NMR dynamic models I. Minimisation algorithms and their performance within the model-free and Brownian rotational diffusion spaces

机译:NMR动态模型的优化I.最小化算法及其在无模型和布朗旋转扩散空间内的性能

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摘要

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 R1, R2 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 () 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-007-9214-2) contains supplementary material, which is available to authorized users.
机译:获得大分子动力学的无模型描述的关键是使用收集的R1,R2和稳态NOE松弛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微不足道,但是,其计算所需的依赖关系的长链使无模型卡方空间复杂化。卷积是由频谱密度函数的洛伦兹形式,某些频谱密度值的线性重组以获得弛豫率,使用这些比率中的两个比率之和计算NOE,最后是chi-的二次形式引起的。平方方程本身。无模型空间的两个主要拓扑特征使优化变得复杂。第一个是一个长长的浅谷,它在无限的相关时间开始,并逐渐接近最小值。最严重的卷积发生在两个时标上,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尽头。将研究大量优化算法,并对其性能进行比较,以确定哪些技术适合用于无模型分析。局部优化算法将显示出足以不仅在无模型空间内最小化,而且对于布朗旋转扩散张量的最小化是足够的。此外,还对Modelfree和Dasha程序的性能进行了研究。确定了许多无模型的优化失败:无法沿极限滑动,Levenberg-Marquardt最小化算法的奇异矩阵失败,两个程序的精度低以及Modelfree中的错误。值得注意的是,当内部相关时间未定义时,Levenberg-Marquardt算法的奇异矩阵故障就会发生,并且在无模型分析中,网格搜索和约束算法都会大大放大奇异矩阵故障。该程序Relax()也作为一个新软件包提供,该软件包旨在通过使用NMR弛豫数据分析大分子动力学,从而减轻了无模型分析中固有的所有问题。电子补充材料本文的在线版本( doi:10.1007 / s10858-007-9214-2)包含补充材料,授权用户可以使用。

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