Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Methods Mol Biol. 2020;2141:347-389. doi: 10.1007/978-1-0716-0524-0_18.
Intrinsically disordered proteins (IDPs) can adopt a range of conformations from globules to swollen coils. This large range of conformational preferences for different IDPs raises the question of how conformational preferences are encoded by sequence. Global compositional features of a sequence such as the fraction of charged residues and the net charge per residue engender certain conformational biases. However, more specific sequence features such as the patterning of oppositely charged residues, expansion driving residues, or residues that can undergo posttranslational modifications can also influence the conformational ensembles of an IDP. Here, we outline how to calculate important global compositional features and patterning metrics that can be used to classify IDPs into different conformational classes and predict relative changes in conformation for sequences with the same amino acid composition. Although increased effort has been devoted to determining conformational properties of IDPs in recent years, quantitative predictions of conformation directly from sequence remain difficult and often inaccurate. Thus, if quantitative predictions of conformational properties are desired, then sequence-specific simulations must be performed.
无规卷曲蛋白质（IDPs）可以从球团到肿胀线圈采用一系列构象。这种不同 IDPs 的构象偏好的大范围提出了序列如何编码构象偏好的问题。序列的全局组成特征，如带电荷残基的分数和每个残基的净电荷，会产生某些构象偏差。然而，更具体的序列特征，如带相反电荷残基的图案、扩张驱动残基或可以进行翻译后修饰的残基，也可以影响 IDP 的构象集合。在这里，我们概述了如何计算重要的全局组成特征和模式度量，可以将 IDP 分类为不同的构象类别，并预测具有相同氨基酸组成的序列的构象相对变化。尽管近年来人们越来越努力确定 IDPs 的构象特性，但直接从序列定量预测构象仍然很困难，而且往往不准确。因此，如果需要定量预测构象特性，则必须进行序列特异性模拟。
