College of Chemistry, Sichuan University, Chengdu 610064, PR China.
Peptides. 2010 Apr;31(4):574-8. doi: 10.1016/j.peptides.2009.12.026. Epub 2010 Jan 4.
In contrast to a large number of classically secreted proteins (CSPs) and non-secreted proteins (NSPs), only a few proteins have been experimentally proved to enter non-classical secretory pathways. So it is difficult to identify non-classically secreted proteins (NCSPs), and no methods are available for distinguishing the three types of proteins simultaneously. In order to solve this problem, a data mining has been taken firstly, and mammalian proteins exported via ER-Golgi-independent pathways are collected through extensive literature searches. In this paper, a support vector machine (SVM)-based ternary classifier named SecretP is proposed to predict mammalian secreted proteins by using pseudo-amino acid composition (PseAA) and five additional features. When distinguishing the three types of proteins, SecretP yielded an accuracy of 88.79%. Evaluating the performance of our method by an independent test set of 92 human proteins, 76 of them are correctly predicted as NCSPs. When performed on another public independent data set, the prediction result of SecretP is comparable to those of other existing computational methods. Therefore, SecretP can be a useful supplementary tool for future secretome studies. The web server SecretP and all supplementary tables listed in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/secretp/index.htm.
与大量经典分泌蛋白（CSPs）和非分泌蛋白（NSPs）相比，只有少数蛋白已被实验证明进入非经典分泌途径。因此，很难识别非经典分泌蛋白（NCSPs），也没有同时区分这三种蛋白的方法。为了解决这个问题，首先进行了数据挖掘，并通过广泛的文献搜索收集了通过 ER-Golgi 非依赖性途径输出的哺乳动物蛋白。本文提出了一种基于支持向量机（SVM）的三元分类器 SecretP，该分类器通过伪氨基酸组成（PseAA）和其他五个附加特征来预测哺乳动物分泌蛋白。在区分这三种蛋白时，SecretP 的准确率为 88.79%。通过对 92 个人类蛋白的独立测试集进行性能评估，其中 76 个蛋白被正确预测为 NCSPs。在对另一个公共独立数据集进行评估时，SecretP 的预测结果可与其他现有计算方法相媲美。因此，SecretP 可以成为未来分泌组学研究的有用辅助工具。SecretP 网络服务器和本文列出的所有补充表格均可在 http://cic.scu.edu.cn/bioinformatics/secretp/index.htm 免费获取。
