Abstract
BackgroundIt has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins.ResultsIn this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively.ConclusionsGpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.
Highlights
It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods
Experimental results show that Gpos-ensemble of classifier chains (ECC)-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively
Datasets In this article, the gram-positive bacterial benchmark dataset used in Gpos-mPLoc [31] and iLoc-Gpos [30] and the gram-negative bacterial benchmark dataset used in GnegmPLoc [26] and iLoc-Gneg [32] are utilized to evaluate the prediction performance of Gpos-ECC-mPLoc and Gneg-ECC-mPLoc respectively
Summary
It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. There exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. Many multi-location proteins are located in the bacterial cells. Multi-location proteins have special biological functions capable of helping the development of new drugs. It is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. Bacteria can be Nowadays, there are two methods for identifying the subcellular locations of proteins: biochemical experiments and computational methods. It is required to develop computational methods to identify the subcellular locations of these proteins automatically and accurately
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