Normal mode analysis (NMA) is a fast and inexpensive approach that is largely used to gain insight into functional protein motions, and more recently to create conformations for further computational studies. However, when the protein structure is unknown, the use of computational models is necessary. Here, we analyze the capacity of NMA in internal coordinate space to predict protein motion, its intrinsic flexibility, and atomic displacements, using protein models instead of native structures, and the possibility to use it for model refinement. Our results show that NMA is quite insensitive to modeling errors, but that calculations are strictly reliable only for very accurate models. Our study also suggests that internal NMA is a more suitable tool for the improvement of structural models, and for integrating them with experimental data or in other computational techniques, such as protein docking or more refined molecular dynamics simulations.

中文翻译：

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对蛋白质结构模型使用正态模式分析。我们的预测能走多远？

正态模式分析 (NMA) 是一种快速且廉价的方法，主要用于深入了解功能性蛋白质运动，最近用于创建构象以进行进一步的计算研究。然而，当蛋白质结构未知时，需要使用计算模型。在这里，我们分析了 NMA 在内部坐标空间中预测蛋白质运动、其内在灵活性和原子位移的能力，使用蛋白质模型而不是天然结构，以及将其用于模型细化的可能性。我们的结果表明，NMA 对建模错误非常不敏感，但只有非常准确的模型才能严格可靠地计算。我们的研究还表明，内部 NMA 是一种更适合改进结构模型的工具，