A large-scale comparison of computational models on the residue flexibility for NMR-derived proteins.
A large-scale comparison of computational models on the residue flexibility for NMR-derived proteins.
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DOI：
10.2174/092986612799080301
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发表时间：
2012-01
影响因子：
1.6
通讯作者：
Hua Zhang;Hanxiao Shi;M. Hanlon
中科院分区：
文献类型：
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作者：
Hua Zhang;Hanxiao Shi;M. Hanlon
作为X射线晶体学的替代方法，核磁共振（NMR）也已成为研究溶液中蛋白质结构和动力学的首选方法。然而，很少有工作使用计算模型，如高斯网络模型（GNM）和机器学习方法集中在NMR衍生的蛋白质来预测残基的灵活性，这是表示相对于平均结构的均方根偏差（RMSD）。我们提供了一个大规模的比较计算模型，包括GNM，无参数GNM和几个线性回归模型使用本地溶剂暴露作为输入，基于1609蛋白质链的数据集，其结构通过NMR解析。结果再次证实，GNM输出与原始RMSD值的相关性优于使用X射线数据的B因子。然而，也可以得出结论，无参数GNM和溶剂暴露为基础的线性回归模型比GNM预测RMSD时，相反，使用X射线数据的结果。NMR和X射线数据之间的残留物的灵活性预测的差异可能是由于其物理和方法的差异相结合。
As an alternative to X-ray crystallography, nuclear magnetic resonance (NMR) has also emerged as the method of choice for studying both protein structure and dynamics in solution. However, little work using computational models such as Gaussian network model (GNM) and machine learning approaches has focused on NMR-derived proteins to predict the residue flexibility, which is represented by the root mean square deviation (RMSD) with respect to the average structure. We provide a large-scale comparison of computational models, including GNM, parameter-free GNM and several linear regression models using local solvent exposures as inputs, based on a dataset of 1609 protein chains whose structures were resolved by NMR. The result again confirmed that the correlation of GNM outputs with raw RMSD values was better than that using B-factors of X-ray data. Nevertheless, it was also concluded that the parameter-free GNM and the solvent exposure based linear regression models performed worse than GNM when predicting RMSD, contrary to results using X-ray data. The discrepancy of residue flexibility prediction between NMR and X-ray data is likely attributable to a combination of their physical and methodological differences.