Xie Dan, Li Ao, Wang Minghui, Fan Zhewen, Feng Huanqing
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W105-10. doi: 10.1093/nar/gki359.
Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at http://Bioinformatics.ustc.edu.cn/LOCSVMPSI/LOCSVMPSI.php.
蛋白质的亚细胞定位是关键的功能特性之一，因为蛋白质必须在亚细胞水平上正确定位才能具有正常的生物学功能。本文介绍了一种名为LOCSVMPSI的新方法，该方法基于支持向量机（SVM）和从PSI-BLAST序列谱生成的位置特异性评分矩阵。通过对RH2427数据集进行留一法检验，LOCSVMPSI获得了90.2%的高总体预测准确率，高于该数据集上SubLoc和ESLpred的预测结果。此外，通过对PK7579数据集进行5折交叉验证测试评估了LOCSVMPSI的预测性能，其预测结果始终优于基于氨基酸和氨基酸对组成的几种支持向量机的先前方法。对SWISSPROT新独特数据集的进一步测试表明，LOCSVMPSI的表现也优于一些广泛使用的预测方法，如PSORTII、TargetP和LOCnet。所有这些结果表明，LOCSVMPSI是预测真核蛋白质亚细胞定位的有力工具。基于该方法开发了一个在线网络服务器（当前版本为1.3），学术和商业用户均可免费使用，可通过http://Bioinformatics.ustc.edu.cn/LOCSVMPSI/LOCSVMPSI.php访问。
