Na Hyuntae, Song Guang
Department of Computer Science, Iowa State University, Ames, Iowa, 50011.
Proteins. 2015 Feb;83(2):259-67. doi: 10.1002/prot.24735. Epub 2014 Dec 18.
Normal mode analysis (NMA) is an important tool for studying protein dynamics. Because of the complexity of conventional NMA that uses an all-atom model and a semi-empirical force field, many simplified NMA models have been developed, some of which are known as elastic network models. The quality of these simplified NMA models was assessed mostly by evaluating their predictions against experimental B-factors, and rarely by comparing them with the original NMA. In this work, we take the effort to create a publicly accessible dataset of proteins with their minimized structures, NMA modes, and mean-square fluctuations. Then, for the first time, we evaluate the quality of individual normal modes of several widely used elastic network models by comparing them with the conventional NMA. Our results demonstrate that the conventional NMA presents a better and more complete evaluation measure of the quality of elastic network models. This realization should be very helpful in improving current or designing new, higher quality elastic network models. Moreover, using the conventional NMA as the standard of evaluation, a number of interesting and significant insights into the elastic network models are gained.
正常模式分析(NMA)是研究蛋白质动力学的重要工具。由于使用全原子模型和半经验力场的传统NMA较为复杂,因此人们开发了许多简化的NMA模型,其中一些被称为弹性网络模型。这些简化NMA模型的质量大多是通过对照实验B因子评估其预测结果来衡量的,很少与原始NMA进行比较。在这项工作中,我们努力创建一个可公开获取的数据集,其中包含蛋白质的最小化结构、NMA模式和均方波动。然后,我们首次通过将几种广泛使用的弹性网络模型的各个正常模式与传统NMA进行比较,来评估它们的质量。我们的结果表明,传统NMA对弹性网络模型的质量提供了更好、更全面的评估标准。这一认识对于改进现有或设计新的、更高质量的弹性网络模型应该非常有帮助。此外,以传统NMA作为评估标准,我们对弹性网络模型有了许多有趣且重要的见解。