College of Chemistry, Sichuan University, Chengdu 610064, PR China.
Comput Biol Med. 2013 Sep;43(9):1177-81. doi: 10.1016/j.compbiomed.2013.06.001. Epub 2013 Jun 11.
In this study, we focus on different types of Gram-negative bacterial secreted proteins, and try to analyze the relationships and differences among them. Through an extensive literature search, 1612 secreted proteins have been collected as a standard data set from three data sources, including Swiss-Prot, TrEMBL and RefSeq. To explore the relationships among different types of secreted proteins, we model this data set as a sequence similarity network. Finally, a multi-classifier named SecretP is proposed to distinguish different types of secreted proteins, and yields a high total sensitivity of 90.12% for the test set. When performed on another public independent dataset for further evaluation, a promising prediction result is obtained. Predictions can be implemented freely online at http://cic.scu.edu.cn/bioinformatics/secretPv2_1/index.htm.
在本研究中，我们专注于不同类型的革兰氏阴性细菌分泌蛋白，并试图分析它们之间的关系和差异。通过广泛的文献检索，从三个数据源（Swiss-Prot、TrEMBL 和 RefSeq）中收集了 1612 个分泌蛋白作为标准数据集。为了探索不同类型分泌蛋白之间的关系，我们将该数据集建模为序列相似性网络。最后，提出了一个名为 SecretP 的多分类器来区分不同类型的分泌蛋白，对测试集的总灵敏度高达 90.12%。当在另一个公共独立数据集上进行进一步评估时，得到了有希望的预测结果。预测可在 http://cic.scu.edu.cn/bioinformatics/secretPv2_1/index.htm 上免费在线实现。
