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无序蛋白质压缩的序列决定因素。

Sequence determinants of compaction in intrinsically disordered proteins.

机构信息

Molecular Structure and Function, Hospital for Sick Children, and Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.

出版信息

Biophys J. 2010 May 19;98(10):2383-90. doi: 10.1016/j.bpj.2010.02.006.

Abstract

Intrinsically disordered proteins (IDPs), which lack folded structure and are disordered under nondenaturing conditions, have been shown to perform important functions in a large number of cellular processes. These proteins have interesting structural properties that deviate from the random-coil-like behavior exhibited by chemically denatured proteins. In particular, IDPs are often observed to exhibit significant compaction. In this study, we have analyzed the hydrodynamic radii of a number of IDPs to investigate the sequence determinants of this compaction. Net charge and proline content are observed to be strongly correlated with increased hydrodynamic radii, suggesting that these are the dominant contributors to compaction. Hydrophobicity and secondary structure, on the other hand, appear to have negligible effects on compaction, which implies that the determinants of structure in folded and intrinsically disordered proteins are profoundly different. Finally, we observe that polyhistidine tags seem to increase IDP compaction, which suggests that these tags have significant perturbing effects and thus should be removed before any structural characterizations of IDPs. Using the relationships observed in this analysis, we have developed a sequence-based predictor of hydrodynamic radius for IDPs that shows substantial improvement over a simple model based upon chain length alone.

摘要

无规卷曲蛋白质(IDPs)缺乏折叠结构,在非变性条件下呈现无规卷曲状态,已被证明在大量细胞过程中发挥着重要作用。这些蛋白质具有有趣的结构特性,与化学变性蛋白质表现出的无规线团样行为不同。特别是,无规卷曲蛋白质通常表现出显著的紧凑性。在这项研究中,我们分析了许多 IDPs 的流体力学半径,以研究这种紧凑性的序列决定因素。净电荷和脯氨酸含量与增加的流体力学半径强烈相关,表明这是紧凑性的主要贡献因素。另一方面,疏水性和二级结构对紧凑性的影响可以忽略不计,这意味着折叠和无规卷曲蛋白质结构的决定因素有很大的不同。最后,我们观察到多组氨酸标签似乎增加了 IDP 的紧凑性,这表明这些标签具有显著的干扰作用,因此在对 IDP 进行任何结构特征分析之前,应该将这些标签去除。利用本分析中观察到的关系,我们开发了一种基于 IDP 序列的流体力学半径预测器,与仅基于链长的简单模型相比,该预测器有了显著的改进。

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