CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, Université de Lyon, Lyon, France.
CiTCoM, CNRS, Université de Paris, Paris, France.
Proteins. 2021 May;89(5):531-543. doi: 10.1002/prot.26037. Epub 2021 Jan 6.
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.
正常模式分析（NMA）是一种快速且廉价的方法，主要用于深入了解功能蛋白运动，最近还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，就需要使用计算模型。在这里，我们使用蛋白质模型而不是天然结构来分析内坐标空间中的 NMA 在预测蛋白质运动、固有灵活性和原子位移方面的能力，以及将其用于模型改进的可能性。我们的结果表明，NMA 对外型误差不敏感，但只有在非常准确的模型下，计算才是严格可靠的。我们的研究还表明，内部 NMA 是改进结构模型的更合适工具，并将其与实验数据或其他计算技术（如蛋白质对接或更精细的分子动力学模拟）集成。
