Markov Models of Amino Acid Substitution to Study Proteins with Intrinsically Disordered Regions

BackgroundIntrinsically disordered proteins (IDPs) or proteins with disordered regions (IDRs) do not have a well-defined tertiary structure, but perform a multitude of functions, often relying on their native disorder to achieve the binding flexibility through changing to alternative conformations. Intrinsic disorder is frequently found in all three kingdoms of life, and may occur in short stretches or span whole proteins. To date most studies contrasting the differences between ordered and disordered proteins focused on simple summary statistics. Here, we propose an evolutionary approach to study IDPs, and contrast patterns specific to ordered protein regions and the corresponding IDRs.ResultsTwo empirical Markov models of amino acid substitutions were estimated, based on a large set of multiple sequence alignments with experimentally verified annotations of disordered regions from the DisProt database of IDPs. We applied new methods to detect differences in Markovian evolution and evolutionary rates between IDRs and the corresponding ordered protein regions. Further, we investigated the distribution of IDPs among functional categories, biochemical pathways and their preponderance to contain tandem repeats.ConclusionsWe find significant differences in the evolution between ordered and disordered regions of proteins. Most importantly we find that disorder promoting amino acids are more conserved in IDRs, indicating that in some cases not only amino acid composition but the specific sequence is important for function. This conjecture is also reinforced by the observation that for of our data set IDRs evolve more slowly than the ordered parts of the proteins, while we still support the common view that IDRs in general evolve more quickly. The improvement in model fit indicates a possible improvement for various types of analyses e.g. de novo disorder prediction using a phylogenetic Hidden Markov Model based on our matrices showed a performance similar to other disorder predictors.研究背景 内在无序蛋白（Intrinsically disordered proteins, IDPs）或含无序区域（disordered regions, IDRs）的蛋白质不具备明确的三级结构，却可执行多种生物学功能，通常依托其天然无序特性，通过构象转换获得结合灵活性。内在无序现象广泛存在于生物的三大域中，既可以以短片段形式存在，也可覆盖整条蛋白质序列。迄今为止，绝大多数对比有序与无序蛋白差异的研究仅聚焦于简单汇总统计。本研究提出一种演化分析方法以研究内在无序蛋白，并对比有序蛋白区域与对应无序区域的演化特征差异。 研究结果 本研究基于源自IDPs数据库DisProt的、经实验验证的无序区域注释信息，结合大规模多序列比对数据集，构建了两种氨基酸替换经验马尔可夫模型。我们采用新方法检测了无序区域与对应有序蛋白区域之间的马尔可夫演化模式与演化速率差异。此外，我们还分析了内在无序蛋白在功能类别、生化通路中的分布特征，以及其富集串联重复序列的倾向。 研究结论 本研究发现蛋白质有序区域与无序区域的演化过程存在显著差异。尤为关键的是，我们观察到促无序氨基酸在无序区域中更为保守，这表明在部分场景下，蛋白质功能的维系不仅依赖氨基酸组成，还与其特定序列密切相关。我们的数据集分析结果显示，无序区域的演化速率慢于蛋白质的有序区域，但这并未推翻学界普遍认为的「无序区域整体演化更快」的观点。模型拟合度的提升表明，该方法可优化多种分析场景——例如基于本研究构建的替换矩阵构建系统发育隐马尔可夫模型（phylogenetic Hidden Markov Model）进行从头（de novo）无序预测，其预测性能与现有主流无序预测工具相当。