a new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites euk-mploc 2.0一种新方法预测真核蛋白质的亚细胞定位单一和多个站点euk-mploc 2.0.pdfVIP

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a new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites euk-mploc 2.0一种新方法预测真核蛋白质的亚细胞定位单一和多个站点euk-mploc 2.0.pdf

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a new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites euk-mploc 2.0一种新方法预测真核蛋白质的亚细胞定位单一和多个站点euk-mploc 2.0

A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 Kuo-Chen Chou1,2*, Hong-Bin Shen1,2 1 Gordon Life Science Institute, San Diego, California, United States of America, 2 Institute of Image Processing Pattern Recognition, Shanghai Jiaotong University, Shanghai, China Abstract Information of subcellular locations of proteins is important for in-depth studies of cell biology. It is very useful for proteomics, system biology and drug development as well. However, most existing methods for predicting protein subcellular location can only cover 5 to 12 location sites. Also, they are limited to deal with single-location proteins and hence failed to work for multiplex proteins, which can simultaneously exist at, or move between, two or more location sites. Actually, multiplex proteins of this kind usually posses some important biological functions worthy of our special notice. A new predictor called ‘‘Euk-mPLoc 2.0’’ is developed by hybridizing the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell wall, (3) centriole, (4) chloroplast, (5) cyanelle, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracell, (11) Golgi apparatus, (12) hydrogenosome, (13) lysosome, (14) melanosome, (15) microsome (16) mitochondria, (17) nucleus, (18) peroxisome, (19) plasma membrane, (20) plastid, (21) spindle pole body, and (22) vacuole. Compared with the existing methods for predicting eukaryotic protein subcellular localization, the new predictor is much more powerful and flexible,