Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Gustav Wieds Vej 14, 8000, Aarhus C, Denmark.
Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark.
Sci Rep. 2019 Mar 26;9(1):5137. doi: 10.1038/s41598-019-41644-w.
Disorder in proteins is vital for biological function, yet it is challenging to characterize. Therefore, methods for predicting protein disorder from sequence are fundamental. Currently, predictors are trained and evaluated using data from X-ray structures or from various biochemical or spectroscopic data. However, the prediction accuracy of disordered predictors is not calibrated, nor is it established whether predictors are intrinsically biased towards one of the extremes of the order-disorder axis. We therefore generated and validated a comprehensive experimental benchmarking set of site-specific and continuous disorder, using deposited NMR chemical shift data. This novel experimental data collection is fully appropriate and represents the full spectrum of disorder. We subsequently analyzed the performance of 26 widely-used disorder prediction methods and found that these vary noticeably. At the same time, a distinct bias for over-predicting order was identified for some algorithms. Our analysis has important implications for the validity and the interpretation of protein disorder, as utilized, for example, in assessing the content of disorder in proteomes.
蛋白质中的无序状态对于生物功能至关重要，但难以进行描述。因此，从序列预测蛋白质无序状态的方法是基础。目前，预测器是使用 X 射线结构或各种生化或光谱数据训练和评估的。然而，无序预测器的预测准确性没有经过校准，也没有确定预测器是否本质上偏向于有序-无序轴的一个极端。因此，我们使用已发表的 NMR 化学位移数据生成并验证了一个全面的、基于实验的、针对特定位置和连续无序的基准数据集。这个新的实验数据集是完全合适的，代表了无序的全貌。随后，我们分析了 26 种广泛使用的无序预测方法的性能，发现这些方法之间存在明显的差异。与此同时，一些算法明显存在过度预测有序的偏差。我们的分析对于蛋白质无序的有效性和解释具有重要意义，例如，在评估蛋白质组中无序含量时。
