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Predicting human protein subcellular localization by heterogeneous and comprehensive approaches

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  • Chi-Hua Tung
  • Chi-Wei Chen
  • Han-Hao Sun
  • Yen-Wei Chu

Abstract

Drug development and investigation of protein function both require an understanding of protein subcellular localization. We developed a system, REALoc, that can predict the subcellular localization of singleplex and multiplex proteins in humans. This system, based on comprehensive strategy, consists of two heterogeneous systematic frameworks that integrate one-to-one and many-to-many machine learning methods and use sequence-based features, including amino acid composition, surface accessibility, weighted sign aa index, and sequence similarity profile, as well as gene ontology function-based features. REALoc can be used to predict localization to six subcellular compartments (cell membrane, cytoplasm, endoplasmic reticulum/Golgi, mitochondrion, nucleus, and extracellular). REALoc yielded a 75.3% absolute true success rate during five-fold cross-validation and a 57.1% absolute true success rate in an independent database test, which was >10% higher than six other prediction systems. Lastly, we analyzed the effects of Vote and GANN models on singleplex and multiplex localization prediction efficacy. REALoc is freely available at http://predictor.nchu.edu.tw/REALoc.

Suggested Citation

  • Chi-Hua Tung & Chi-Wei Chen & Han-Hao Sun & Yen-Wei Chu, 2017. "Predicting human protein subcellular localization by heterogeneous and comprehensive approaches," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0178832
    DOI: 10.1371/journal.pone.0178832
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    References listed on IDEAS

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    1. Kuo-Chen Chou & Hong-Bin Shen, 2010. "A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-9, April.
    2. Jianjun He & Hong Gu & Wenqi Liu, 2012. "Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-10, June.
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