Petersen Mark, Barrick Doug
Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA.
T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA; email:
Annu Rev Biophys. 2021 May 6;50:245-265. doi: 10.1146/annurev-biophys-102220-083020. Epub 2021 Feb 19.
Cooperativity is a hallmark of protein folding, but the thermodynamic origins of cooperativity are difficult to quantify. Tandem repeat proteins provide a unique experimental system to quantify cooperativity due to their internal symmetry and their tolerance of deletion, extension, and in some cases fragmentation into single repeats. Analysis of repeat proteins of different lengths with nearest-neighbor Ising models provides values for repeat folding ([Formula: see text]) and inter-repeat coupling (Δ). In this article, we review the architecture of repeat proteins and classify them in terms of Δ and Δ; this classification scheme groups repeat proteins according to their degree of cooperativity. We then present various statistical thermodynamic models, based on the 1D-Ising model, for analysis of different classes of repeat proteins. We use these models to analyze data for highly and moderately cooperative and noncooperative repeat proteins and relate their fitted parameters to overall structural features.
协同性是蛋白质折叠的一个标志，但协同性的热力学起源难以量化。串联重复蛋白由于其内部对称性以及对缺失、延伸的耐受性，在某些情况下还能断裂成单个重复序列，从而提供了一个独特的实验系统来量化协同性。用最近邻伊辛模型分析不同长度的重复蛋白可得出重复序列折叠（[公式：见正文]）和重复序列间耦合（Δ）的值。在本文中，我们综述了重复蛋白的结构，并根据Δ和Δ对其进行分类；这种分类方案根据重复蛋白的协同程度对它们进行分组。然后，我们基于一维伊辛模型提出各种统计热力学模型，用于分析不同类别的重复蛋白。我们使用这些模型来分析高度协同、中度协同和非协同重复蛋白的数据，并将其拟合参数与整体结构特征联系起来。
