Eyal Eran, Dutta Anindita, Bahar Ivet
Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Cancer Research Institute, Sheba Medical Center, Ramat Gan, Israel.
Wiley Interdiscip Rev Comput Mol Sci. 2011 May-Jun;1(3):426-439. doi: 10.1002/wcms.44. Epub 2011 Apr 11.
Recent years have seen a significant increase in the number of computational studies that adopted network models for investigating biomolecular systems dynamics and interactions. In particular, elastic network models have proven useful in elucidating the dynamics and allosteric signaling mechanisms of proteins and their complexes. Here we present an overview of two most widely used elastic network models, the Gaussian Network Model (GNM) and Anisotropic Network Model (ANM). We illustrate their use in (i) explaining the anisotropic response of proteins observed in external pulling experiments, (ii) identifying residues that possess high allosteric potentials, and demonstrating in this context the propensity of catalytic sites and metal-binding sites for enabling efficient signal transduction, and (iii) assisting in structure refinement, molecular replacement and comparative modeling of ligand-bound forms via efficient sampling of energetically favored conformers.
近年来，采用网络模型来研究生物分子系统动力学和相互作用的计算研究数量显著增加。特别是，弹性网络模型已被证明在阐明蛋白质及其复合物的动力学和变构信号传导机制方面很有用。在这里，我们概述两种使用最广泛的弹性网络模型，即高斯网络模型（GNM）和各向异性网络模型（ANM）。我们举例说明它们在以下方面的应用：（i）解释在外部拉伸实验中观察到的蛋白质各向异性响应；（ii）识别具有高变构潜力的残基，并在此背景下证明催化位点和金属结合位点实现有效信号转导的倾向；（iii）通过对能量有利构象的有效采样，协助进行配体结合形式的结构优化、分子置换和比较建模。
