Huang Hsien-Da, Lee Tzong-Yi, Tzeng Shih-Wei, Horng Jorng-Tzong
Department of Biological Science and Technology, Institute of Bioinformatics, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W226-9. doi: 10.1093/nar/gki471.
KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/.
KinasePhos是一个用于通过计算识别催化激酶特异性磷酸化位点的新型网络服务器。来自公共领域数据源的已知磷酸化位点按其注释的蛋白激酶进行分类。基于轮廓隐马尔可夫模型，从磷酸化位点的激酶特异性组中学习计算模型。在评估所学习的模型之后，从每个激酶特异性组中选择具有最高准确性的模型，用于基于网络的预测工具以识别蛋白质磷酸化位点。因此，这项工作开发了一种具有高灵敏度和特异性的激酶特异性磷酸化位点预测工具。该预测工具可在http://KinasePhos.mbc.nctu.edu.tw/免费获取。
