LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST. 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.

References in zbMATH (referenced in 11 articles )

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  1. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  2. Arango-Argoty, G. A.; Jaramillo-Garzón, J. A.; Castellanos-Domínguez, G.: Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins (2015)
  3. Kavousi, Kaveh; Sadeghi, Mehdi; Moshiri, Behzad; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: Evidence theoretic protein fold classification based on the concept of hyperfold (2012)
  4. Kavousi, Kaveh; Moshiri, Behzad; Sadeghi, Mehdi; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM (2011)
  5. Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi: Prediction of protein submitochondria locations based on data fusion of various features of sequences (2011)
  6. Blum, Torsten; Briesemeister, Sebastian; Kohlbacher, Oliver: Multiloc2: integrating phylogeny and gene ontology terms improves subcellular protein localization prediction (2009) ioport
  7. Du, Pufeng; Cao, Shengjiao; Li, Yanda: SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic (K)-nearest neighbor (ET-KNN) algorithm (2009)
  8. Kumar, Manish; Raghava, Gajendra P. S.: Prediction of nuclear proteins using SVM and HMM models (2009) ioport
  9. Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang: Semi-supervised protein subcellular localization (2009) ioport
  10. Ou, Yu-Yen; Gromiha, M. Michael; Chen, Shu-An; Suwa, Makiko: TMBETADISC-RBF: discrimination of (\beta)-barrel membrane proteins using RBF networks and PSSM profiles (2008)
  11. Xie, Dan; Li, Ao; Wang, Minghui; Fan, Zhewen; Feng, Huanqing: LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST. (2005) ioport