Predicting human protein subcellular localization by heterogeneous and comprehensive approaches

Chi-Hua Tung, Chi-Wei Chen, Han-Hao Sun and Yen-Wei Chu

PLOS ONE, 2017, vol. 12, issue 6, 1-14

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0178832

DOI: 10.1371/journal.pone.0178832

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