Protein disorder prediction at multiple levels of sensitivity and specificity.
Protein disorder prediction at multiple levels of sensitivity and specificity.
复制标题
蛋白质障碍在多个敏感性和特异性水平上的预测。
DOI：
10.1186/1471-2164-9-s1-s9
复制
发表时间：
2008
期刊：
影响因子：
4.4
通讯作者：
Cheng, Jianlin
中科院分区：
文献类型：
--
作者：
Hecker, Joshua;Yang, Jack Y.;Cheng, Jianlin
许多蛋白质区域和整个蛋白质没有明确的三级结构，而是在不同的生理化学环境下以动态的、无序的集合体形式存在。这些蛋白质紊乱区域的鉴定对于蛋白质生产、蛋白质结构预测和确定以及蛋白质功能注释具有重要意义。自从Dunker的实验室在1997年设计了第一个预测器以来，已经开发了许多不同的疾病预测软件和网络服务。然而，大多数软件包使用预定义的阈值来选择有序或无序的残基。在许多情况下，用户需要选择不同灵敏度和特异性水平的有序或无序残基。在这里，我们基准的最先进的疾病预测，DISpro，从蛋白质数据库创建的大型蛋白质疾病数据集，并系统地评估灵敏度和特异性的关系。此外，我们扩展其功能，允许用户通过设置不同的决策阈值来权衡特异性和敏感性。此外，我们比较DISpro与其他七个自动化的疾病预测95蛋白质的目标，在第七版的关键评估技术蛋白质结构预测（CASP7）。DISpro被列为最佳预测因子之一。DISpro的评价和扩展使其成为结构和功能基因组学研究中更有价值和有用的工具。
Many protein regions and some entire proteins have no definite tertiary structure, existing instead as dynamic, disorder ensembles under different physiochemical circumstances. Identification of these protein disorder regions is important for protein production, protein structure prediction and determination, and protein function annotation. A number of different disorder prediction software and web services have been developed since the first predictor was designed by Dunker's lab in 1997. However, most of the software packages use a pre-defined threshold to select ordered or disordered residues. In many situations, users need to choose ordered or disordered residues at different sensitivity and specificity levels. Here we benchmark a state of the art disorder predictor, DISpro, on a large protein disorder dataset created from Protein Data Bank and systematically evaluate the relationship of sensitivity and specificity. Also, we extend its functionality to allow users to trade off specificity and sensitivity by setting different decision thresholds. Moreover, we compare DISpro with seven other automated disorder predictors on the 95 protein targets used in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7). DISpro is ranked as one of the best predictors. The evaluation and extension of DISpro make it a more valuable and useful tool for structural and functional genomics.