Protein subcellular localization aims at predicting the location of a protein within a cell using computational
methods. Knowledge of subcellular localization of viral proteins in a host cell or virus-infected cell is important because it
is closely related to their destructive tendencies and consequences. Prediction of viral protein subcellular localization is an
important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more
different subcellular location sites. Most of the existing protein subcellular localization methods specialized for viral proteins
are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, a
new predictor, called Virus-ECC-mPLoc, has been developed that can be used to deal with the systems containing both
singleplex and multiplex proteins by introducing a powerful multi-label learning approach which exploits correlations between
subcellular locations and by hybridizing the gene ontology information with the dipeptide composition information.
It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3)
host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. Experimental results show that the
overall success rates thus obtained by Virus-ECC-mPLoc are 86.9% for jackknife test and 87.2% for independent data set
test, which are significantly higher than that by any of the existing predictors. As a user-friendly web-server, Virus-ECCmPLoc
is freely accessible to the public at the web-site http://levis.tongji.edu.cn:8080/bioinfo/Virus-ECC-mPLoc/.
Keywords: Protein subcellular localization, multi-label learning, classifier chain, multiplex proteins, computational method, viral protein, virus-infected cell, Virus-ECC-mPLoc, dipeptide, gene ontology
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