Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application.
Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application.
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DOI：
10.1021/acs.jpcb.8b01763
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发表时间：
2018-04-12
期刊：
影响因子：
--
通讯作者：
Lindert S
中科院分区：
文献类型：
--
作者：
Kim SS;Seffernick JT;Lindert S
尽管许多蛋白质需要良好折叠的结构来适当地激发其生物功能，但很大一部分功能蛋白质包含区域-被称为内在无序的蛋白质区域-不太可能形成稳定的结构。内在无序蛋白的显著功能作用是转录调节、翻译和细胞信号转导。此外，内在无序的蛋白质区域在与各种人类疾病相关的许多蛋白质中非常丰富，因此这些片段已成为潜在治疗方法的有吸引力的药物靶点。在过去的几十年里，已经开发了许多计算方法来准确预测蛋白质的无序区域。在这里，我们介绍了一种使用结构预测软件Rosetta的用户友好和可靠的方法来预测无序蛋白质区域。使用来自基准数据集（16个DisProt数据库蛋白质）和测试数据集（229个具有NMR数据的蛋白质）的245个蛋白质，我们使用Rosetta预测全局蛋白质结构，然后显示在无序区域和有序区域的Rosetta得分之间存在统计学显著差异，无序区域的得分不太有利。此外，有序和无序蛋白质区域之间的分数差异足以准确识别无序蛋白质区域。结果表明，我们的Rosetta ResidueDisorder方法（基准数据集预测准确率为71.77%，独立测试数据集预测准确率为65.37%）优于其他现有的无序预测工具，并且对有序或无序区域都没有出现偏倚预测。为了方便使用，已经为Rosetta残差法开发了一个Rosetta应用程序。
Although many proteins necessitate well-folded structures to properly instigate their biological functions, a large fraction of functioning proteins contain regions – known as intrinsically disordered protein regions – where stable structures are not likely to form. Notable functional roles of intrinsically disordered proteins are in transcriptional regulation, translation, and cellular signal transduction. Moreover, intrinsically disordered protein regions are highly abundant in many proteins associated with various human diseases, therefore these segments have become attractive drug targets for potential therapeutics. Over the past decades, numerous computational methods have been developed to accurately predict disordered regions of proteins. Here we introduce a user-friendly and reliable approach for the prediction of disordered protein regions using the structure prediction software Rosetta. Using 245 proteins from a benchmark dataset (16 DisProt database proteins) and a test dataset (229 proteins with NMR data), we use Rosetta to predict the global protein structures and then show that there is a statistically significant difference between Rosetta scores in disordered and ordered regions, with scores being less favorable in disordered regions. Furthermore, the difference in scores between ordered and disordered protein regions is sufficient to accurately identify disordered protein regions. As a result, our Rosetta ResidueDisorder method (benchmark dataset prediction accuracy of 71.77% and independent test dataset prediction accuracy of 65.37%) outperformed other established disorder prediction tools and did not exhibit a biased prediction toward either ordered or disordered regions. To facilitate usage, a Rosetta application has been developed for the Rosetta ResidueDisorder method.
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