Li Sujun, Liu Boshu, Zeng Rong, Cai Yudong, Li Yixue
Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China.
Comput Biol Chem. 2006 Jun;30(3):203-8. doi: 10.1016/j.compbiolchem.2006.02.002.
O-glycosylation is one of the most important, frequent and complex post-translational modifications. This modification can activate and affect protein functions. Here, we present three support vector machines models based on physical properties, 0/1 system, and the system combining the above two features. The prediction accuracies of the three models have reached 0.82, 0.85 and 0.85, respectively. The accuracies of the three SVMs methods were evaluated by 'leave-one-out' cross validation. This approach provides a useful tool to help identify the O-glycosylation sites in mammalian proteins. An online prediction web server is available at http://www.biosino.org/Oglyc.
O-糖基化是最重要、最常见且最复杂的翻译后修饰之一。这种修饰能够激活并影响蛋白质功能。在此，我们展示了基于物理性质、0/1系统以及结合上述两种特征的系统构建的三种支持向量机模型。这三种模型的预测准确率分别达到了0.82、0.85和0.85。这三种支持向量机方法的准确率通过“留一法”交叉验证进行评估。该方法提供了一个有用的工具，有助于识别哺乳动物蛋白质中的O-糖基化位点。可通过http://www.biosino.org/Oglyc访问在线预测网络服务器。
