Abstract
Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the “multi-label scale” and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of Gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg. The dataset contains 1,392 Gram-negative bacterial proteins classified into the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. Of the 1,392 proteins, 1,328 are each with only one subcellular location and the other 64 are each with two subcellular locations, but none of the proteins included has pairwise sequence identity to any other in a same subset (subcellular location). It was observed that the overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gneg was over 91%, which is about 6% higher than that by Gneg-mPLoc. As a user-friendly web-server, iLoc-Gneg is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Gneg. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Gneg web-server also has the function to accept the batch job submission, which is not available in the existing version of Gneg-mPLoc web-server. It is anticipated that iLoc-Gneg may become a useful high throughput tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development.
Highlights
Bacteria can be divided into two groups: Gram-positive and Gram-negative
Gram-positive bacteria are those that are stained dark blue or violet by Gram staining; while Gram-negative bacteria cannot retain the stain, instead taking up the counterstain and appearing red or pink. It has special meaning for both basic research and drug design to study bacteria because (1) they are the workhorses for the fields of molecular biology, biochemistry, and genetics due to their ability to quickly grow and being relatively easier to be manipulated, and (2) they are both harmful and useful
With the explosion of protein sequences generated in the post-genomic era, we are challenged to develop computational methods for timely and accurately identifying the subcellular locations of newly discovered bacterial proteins based on their sequence information alone because this kind of knowledge will be very useful for selecting proper bacterial proteins for a special target, or screening and prioritizing candidates in drug design
Summary
Gram-positive bacteria are those that are stained dark blue or violet by Gram staining; while Gram-negative bacteria cannot retain the stain, instead taking up the counterstain and appearing red or pink. It has special meaning for both basic research and drug design to study bacteria because (1) they are the workhorses for the fields of molecular biology, biochemistry, and genetics due to their ability to quickly grow and being relatively easier to be manipulated, and (2) they are both harmful and useful. As well as the long list of references cited in the two review papers) Those that are specialized for dealing with Gramnegative proteins are only a few. Gneg-PLoc was able to yield higher success rates
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