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標題: Predicting human protein subcellular localization by heterogeneous and comprehensive approaches
作者: Tung, Chi-Hua
Chen, Chi-Wei
Sun, Han-Hao
Chu, Yen-Wei
關鍵字: Amino Acids;Humans;Subcellular Fractions
Project: PloS one, Volume 12, Issue 6, Page(s) e0178832.
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
DOI: 10.1371/journal.pone.0178832
Appears in Collections:基因體暨生物資訊學研究所

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