Fuglebakk Edvin, Tiwari Sandhya P, Reuter Nathalie
Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.
Biochim Biophys Acta. 2015 May;1850(5):911-922. doi: 10.1016/j.bbagen.2014.09.021. Epub 2014 Sep 28.
Elastic network models (ENMs) are based on the simple idea that a protein can be described as a set of particles connected by springs, which can then be used to describe its intrinsic flexibility using, for example, normal mode analysis. Since the introduction of the first ENM by Monique Tirion in 1996, several variants using coarser protein models have been proposed and their reliability for the description of protein intrinsic dynamics has been widely demonstrated. Lately an increasing number of studies have focused on the meaning of slow dynamics for protein function and its potential conservation through evolution. This leads naturally to comparisons of the intrinsic dynamics of multiple protein structures with varying levels of similarity.
We describe computational strategies for calculating and comparing intrinsic dynamics of multiple proteins using elastic network models, as well as a selection of examples from the recent literature.
The increasing interest for comparing dynamics across protein structures with various levels of similarity, has led to the establishment and validation of reliable computational strategies using ENMs. Comparing dynamics has been shown to be a viable way for gaining greater understanding for the mechanisms employed by proteins for their function. Choices of ENM parameters, structure alignment or similarity measures will likely influence the interpretation of the comparative analysis of protein motion.
Understanding the relation between protein function and dynamics is relevant to the fundamental understanding of protein structure-dynamics-function relationship. This article is part of a Special Issue entitled Recent developments of molecular dynamics.
弹性网络模型(ENM)基于一个简单的理念,即蛋白质可以被描述为一组由弹簧连接的粒子,然后可以用例如正常模式分析来描述其内在柔韧性。自1996年莫妮克·蒂里翁提出第一个ENM以来,已经提出了几种使用更粗糙蛋白质模型的变体,并且它们在描述蛋白质内在动力学方面的可靠性已得到广泛证明。最近,越来越多的研究集中在蛋白质功能的慢动力学的意义及其在进化过程中的潜在保守性上。这自然导致了对具有不同相似程度的多个蛋白质结构的内在动力学进行比较。
我们描述了使用弹性网络模型计算和比较多个蛋白质内在动力学的计算策略,以及从最近文献中选取的一些例子。
对比较不同相似程度蛋白质结构的动力学的兴趣日益增加,已导致使用ENM建立并验证了可靠的计算策略。比较动力学已被证明是更深入理解蛋白质发挥功能所采用机制的一种可行方法。ENM参数、结构比对或相似性度量的选择可能会影响对蛋白质运动比较分析的解释。
理解蛋白质功能与动力学之间的关系对于从根本上理解蛋白质结构 - 动力学 - 功能关系至关重要。本文是名为“分子动力学的最新进展”的特刊的一部分。