Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins.
By incorporating the information of gene ontology, functional domain, and sequential evolution, a new predictor called Gneg-mPLoc was developed. It can be used to identify Gram-negative bacterial proteins among the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. It can also be used to deal with the case when a query protein may simultaneously exist in more than one location. Compared with the original predictor called Gneg-PLoc, the new predictor is much more powerful and flexible. For a newly constructed stringent benchmark dataset in which none of proteins included has >or=25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gneg-mPLoc was 85.5%, which was more than 14% higher than the corresponding rate by the Gneg-PLoc. As a user friendly web-server, Gneg-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gneg-multi/.
Shen
Hong-Bin
HB
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China. hbshen@sjtu.edu.cn
Chou
Kuo-Chen
KC
D000465:Algorithms; D001426:Bacterial Proteins / Q000378:metabolism*; D002462:Cell Membrane / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003593:Cytoplasm / Q000378:metabolism; D005110:Extracellular Space / Q000378:metabolism; D010861:Fimbriae, Bacterial / Q000378:metabolism; D005407:Flagella / Q000378:metabolism; D006090:Gram-Negative Bacteria / Q000378:metabolism*; D020407:Internet
D001426:Bacterial Proteins