Saunders Neil F W, Brinkworth Ross I, Huber Thomas, Kemp Bruce E, Kobe Bostjan
School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia.
BMC Bioinformatics. 2008 May 26;9:245. doi: 10.1186/1471-2105-9-245.
We have previously described an approach to predicting the substrate specificity of serine-threonine protein kinases. The method, named Predikin, identifies key conserved substrate-determining residues in the kinase catalytic domain that contact the substrate in the region of the phosphorylation site and so determine the sequence surrounding the phosphorylation site. Predikin was implemented originally as a web application written in Javascript.
Here, we describe a new version of Predikin, completely revised and rewritten as a modular framework that provides multiple enhancements compared with the original. Predikin now consists of two components: (i) PredikinDB, a database of phosphorylation sites that links substrates to kinase sequences and (ii) a Perl module, which provides methods to classify protein kinases, reliably identify substrate-determining residues, generate scoring matrices and score putative phosphorylation sites in query sequences. The performance of Predikin as measured using receiver operator characteristic (ROC) graph analysis equals or surpasses that of existing comparable methods. The Predikin website has been redesigned to incorporate the new features.
New features in Predikin include the use of SQL queries to PredikinDB to generate predictions, scoring of predictions, more reliable identification of substrate-determining residues and putative phosphorylation sites, extended options to handle protein kinase and substrate data and an improved web interface. The new features significantly enhance the ability of Predikin to analyse protein kinases and their substrates. Predikin is available at http://predikin.biosci.uq.edu.au.
我们之前描述了一种预测丝氨酸 - 苏氨酸蛋白激酶底物特异性的方法。该方法名为Predikin，可识别激酶催化结构域中关键的保守底物决定残基，这些残基在磷酸化位点区域与底物接触，从而决定磷酸化位点周围的序列。Predikin最初是用JavaScript编写的一个网络应用程序。
在此，我们描述了Predikin的一个新版本，它经过全面修订并改写为一个模块化框架，与原始版本相比有多项改进。Predikin现在由两个组件组成：（i）PredikinDB，一个磷酸化位点数据库，将底物与激酶序列相链接；（ii）一个Perl模块，它提供对蛋白激酶进行分类、可靠识别底物决定残基、生成评分矩阵以及对查询序列中的假定磷酸化位点进行评分的方法。使用受试者工作特征（ROC）图分析衡量，Predikin的性能等于或超过现有同类方法。Predikin网站已重新设计以纳入新功能。
Predikin的新功能包括使用SQL查询PredikinDB来生成预测、对预测进行评分、更可靠地识别底物决定残基和假定磷酸化位点、处理蛋白激酶和底物数据的扩展选项以及改进的网络界面。这些新功能显著增强了Predikin分析蛋白激酶及其底物的能力。可通过http://predikin.biosci.uq.edu.au获取Predikin。
