Chen Jianhan, Brooks Charles L, Wright Peter E
Department of Molecular Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
J Biomol NMR. 2004 Jul;29(3):243-57. doi: 10.1023/B:JNMR.0000032504.70912.58.
The popular model-free approach to analyze NMR relaxation measurements has been examined using artificial amide (15)N relaxation data sets generated from a 10 nanosecond molecular dynamics trajectory of a dihydrofolate reductase ternary complex in explicit water. With access to a detailed picture of the underlying internal motions, the efficacy of model-free analysis and impact of model selection protocols on the interpretation of NMR data can be studied. In the limit of uncorrelated global tumbling and internal motions, fitting the relaxation data to the model-free models can recover a significant amount of quantitative information on the internal dynamics. Despite a slight overestimation, the generalized order parameter is quite accurately determined. However, the model-free analysis appears to be insensitive to the presence of nanosecond time scale motions with relatively small magnitude. For such cases, the effective correlation time can be significantly underestimated. As a result, proteins appear to be more rigid than they really are. The model selection protocols have a major impact on the information one can reliably obtain. The commonly employed protocol based on step-up hypothesis testing has severe drawbacks of oversimplification and underfitting. The consequences are that the order parameter is more severely overestimated and the correlation time more severely underestimated. Instead, model selection based on Bayesian Information Criteria (BIC), recently introduced to the model-free analysis by d'Auvergne and Gooley (2003), provides a better balance between bias and variance. More appropriate models can be selected, leading to improved estimate of both the order parameter and correlation time. In addition, the computational cost is significantly reduced and subjective parameters such as the significance level are unnecessary.
我们使用在明确水环境中从二氢叶酸还原酶三元复合物的10纳秒分子动力学轨迹生成的人工酰胺（15）N弛豫数据集，检验了用于分析核磁共振弛豫测量的流行的无模型方法。通过获取基础内部运动的详细图像，可以研究无模型分析的功效以及模型选择协议对核磁共振数据解释的影响。在不相关的整体翻滚和内部运动的极限情况下，将弛豫数据拟合到无模型模型可以恢复大量关于内部动力学的定量信息。尽管有轻微高估，但广义序参量的确定相当准确。然而，无模型分析似乎对幅度相对较小的纳秒时间尺度运动的存在不敏感。对于这种情况，有效相关时间可能会被严重低估。结果，蛋白质看起来比实际情况更刚性。模型选择协议对人们能够可靠获得的信息有重大影响。基于逐步假设检验的常用协议存在过度简化和拟合不足的严重缺点。其后果是序参量被更严重地高估，相关时间被更严重地低估。相反，基于贝叶斯信息准则（BIC）的模型选择，最近由d'Auvergne和Gooley（2003）引入到无模型分析中，在偏差和方差之间提供了更好的平衡。可以选择更合适的模型，从而改进序参量和相关时间的估计。此外，计算成本显著降低，并且诸如显著性水平等主观参数不再必要。
