Na Hyuntae, Song Guang
Department of Computer Science, Iowa State University, Ames, Iowa, 50011.
Proteins. 2014 Sep;82(9):2157-68. doi: 10.1002/prot.24571. Epub 2014 Apr 21.
Normal mode analysis (NMA) has been a powerful tool for studying protein dynamics. Elastic network models (ENM), through their simplicity, have made normal mode computations accessible to a much broader research community and for many more biomolecular systems. The drawback of ENMs, however, is that they are less accurate than NMA. In this work, through steps of simplification that starts with NMA and ends with ENMs we build a tight connection between NMA and ENMs. In the process of bridging between the two, we have also discovered several high-quality simplified models. Our best simplified model has a mean correlation with the original NMA that is as high as 0.88. In addition, the model is force-field independent and does not require energy minimization, and thus can be applied directly to experimental structures. Another benefit of drawing the connection is a clearer understanding why ENMs work well and how it can be further improved. We discovered that ANM can be greatly enhanced by including an additional torsional term and a geometry term.
正常模式分析（NMA）一直是研究蛋白质动力学的有力工具。弹性网络模型（ENM）凭借其简单性，使正常模式计算能够为更广泛的研究群体所使用，并且可用于更多的生物分子系统。然而，ENM的缺点是其准确性不如NMA。在这项工作中，我们通过从NMA开始到ENM结束的一系列简化步骤，建立了NMA和ENM之间的紧密联系。在连接两者的过程中，我们还发现了几个高质量的简化模型。我们最好的简化模型与原始NMA的平均相关性高达0.88。此外，该模型与力场无关，不需要能量最小化，因此可以直接应用于实验结构。建立这种联系的另一个好处是能更清楚地理解为什么ENM效果良好以及如何进一步改进。我们发现，通过加入额外的扭转项和几何项，可以大大增强各向异性网络模型（ANM）。
