Chennubhotla Chakra, Rader A J, Yang Lee-Wei, Bahar Ivet
Department of Computational Biology, School of Medicine, University of Pittsburgh, W1040 BST 200 Lothrop Street, Pittsburgh, PA 15261, USA.
Phys Biol. 2005 Nov 9;2(4):S173-80. doi: 10.1088/1478-3975/2/4/S12.
With advances in structure genomics, it is now recognized that knowledge of structure alone is insufficient to understand and control the mechanisms of biomolecular function. Additional information in the form of dynamics is needed. As demonstrated in a large number of studies, the machinery of proteins and their complexes can be understood to a good approximation by adopting Gaussian (or elastic) network models (GNM) for simplified normal mode analyses. While this approximation lacks chemical details, it provides us with a means for assessing the collective motions of large structures/assemblies and perform a comparative analysis of a series of proteins, thus providing insights into the mechanical aspects of biomolecular dynamics. In this paper, we discuss recent applications of GNM to a series of enzymes as well as large structures such as the HK97 bacteriophage viral capsids. Understanding the dynamics of large protein structures can be computationally challenging. To this end, we introduce a new approach for building a hierarchical, reduced rank representation of the protein topology and consequently the fluctuation dynamics.
随着结构基因组学的进展，现在人们认识到仅靠结构知识不足以理解和控制生物分子功能的机制。还需要动力学形式的额外信息。正如大量研究所表明的，通过采用高斯（或弹性）网络模型（GNM）进行简化的正常模式分析，可以很好地近似理解蛋白质及其复合物的机制。虽然这种近似缺乏化学细节，但它为我们提供了一种评估大型结构/组件集体运动的方法，并对一系列蛋白质进行比较分析，从而深入了解生物分子动力学的力学方面。在本文中，我们讨论了GNM在一系列酶以及诸如HK97噬菌体病毒衣壳等大型结构上的最新应用。理解大型蛋白质结构的动力学在计算上具有挑战性。为此，我们引入了一种新方法，用于构建蛋白质拓扑结构的分层、降秩表示，进而构建波动动力学。
