MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein’s subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage. Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism

References in zbMATH (referenced in 11 articles )

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  1. Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition (2018)
  2. Han, Guo-Sheng; Yu, Zu-Guo; Anh, Vo: A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC (2014)
  3. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  4. Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)
  5. Mei, Suyu: Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based multi-label homolog knowledge transfer learning (2012)
  6. Mei, S.; Wang, F.; Zhou, S.: Gene ontology based transfer learning for protein subcellular localization (2011) ioport
  7. Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi: Prediction of protein submitochondria locations based on data fusion of various features of sequences (2011)
  8. Blum, Torsten; Briesemeister, Sebastian; Kohlbacher, Oliver: Multiloc2: integrating phylogeny and gene ontology terms improves subcellular protein localization prediction (2009) ioport
  9. Buske, Fabian A.; Maetschke, Stefan; Bodén, Mikael: It’s about time: signal recognition in staged models of protein translocation (2009)
  10. Dogruel, Mutlu; Down, Thomas A.; Hubbard, Tim J. P.: Nestedmica as an ab initio protein motif discovery tool (2008) ioport
  11. Liu, Junfeng; Zhao, Hongyu; Tan, Jun; Luo, Dajie; Yu, Weichuan; Harner, E. James; Shih, Weichung Joe: Is subcellular localization informative for modeling protein-protein interaction signal? (2008) ioport