The use of model selection in the model-free analysis of protein dynamics
The use of model selection in the model-free analysis of protein dynamics
复制标题
DOI：
10.1023/a:1021902006114
复制
发表时间：
2003-01-01
影响因子：
2.7
通讯作者：
Gooley, PR
中科院分区：
文献类型：
--
作者：
d'Auvergne, EJ;Gooley, PR
NMR弛豫数据的无模型分析广泛用于蛋白质动力学的研究，包括从相对于扩散框架的内部运动中分离出全局旋转扩散，以及通过振幅和时间尺度描述这些内部运动。存在五种无模型模型，每种模型描述不同类型的运动。无模型分析需要选择最能描述NH键动力学的模型。它将被证明，目前使用的模型选择技术有两个明显的缺陷，拟合不足，而不是选择一个模型时，应该选择。欠拟合打破了简约原则，导致最终无模型结果出现偏差，表现为S-2的高估以及tau（e）和R-ex的低估。因此，蛋白质虚假地看起来比实际上更坚硬。模型选择在其他领域也得到了广泛的发展。被称为Akaike信息标准（AIC），小样本量校正AIC（AICc），贝叶斯信息标准（BIC），自助法和交叉验证的技术将与目前使用的技术进行比较。为了分析各种技术，创建了覆盖所有无模型运动的合成噪声数据。数据由两种类型的三维网格组成，R-ex网格覆盖具有化学交换的单运动{S-2，tau（e），R-ex}，而Double Motion网格覆盖两个内部运动{S-f（2），S-s（2），tau（s）}。比较的结论是，对于准确的无模型结果，AIC模型选择是必不可少的。由于AIC方法既不欠拟合，也不过拟合，因此它是应用奥卡姆剃刀的最佳工具，并且具有简化和加速无模型分析的额外好处。
Model-free analysis of NMR relaxation data, which is widely used for the study of protein dynamics, consists of the separation of the global rotational diffusion from internal motions relative to the diffusion frame and the description of these internal motions by amplitude and timescale. Five model-free models exist, each of which describes a different type of motion. Model-free analysis requires the selection of the model which best describes the dynamics of the NH bond. It will be demonstrated that the model selection technique currently used has two significant flaws, under-fitting, and not selecting a model when one ought to be selected. Under-fitting breaks the principle of parsimony causing bias in the final model-free results, visible as an overestimation of S-2 and an underestimation Of tau(e) and R-ex. As a consequence the protein falsely appears to be more rigid than it actually is. Model selection has been extensively developed in other fields. The techniques known as Akaike's Information Criteria (AIC), small sample size corrected AIC (AICc), Bayesian Information Criteria (BIC), bootstrap methods, and cross-validation will be compared to the currently used technique. To analyse the variety of techniques, synthetic noisy data covering all model-free motions was created. The data consists of two types of three-dimensional grid, the R-ex grids covering single motions with chemical exchange {S-2, tau(e), R-ex}, and the Double Motion grids covering two internal motions {S-f(2), S-s(2), tau(s)}. The conclusion of the comparison is that for accurate model-free results, AIC model selection is essential. As the method neither under, nor over-fits, AIC is the best tool for applying Occam's razor and has the additional benefits of simplifying and speeding up model-free analysis.