title: Large-scale predictions of gram-negative bacterial protein subcellular locations.
abstract: Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells. Therefore, developing an automated method for fast and reliable prediction of Gram-negative protein subcellular location will allow us to not only timely annotate gene products, but also screen candidates for drug discovery. However, protein subcellular location prediction is a very difficult problem, particularly when more location sites need to be involved and when unknown query proteins do not have significant homology to proteins of known subcellular locations. PSORT-B, a recently updated version of PSORT, widely used for predicting Gram-negative protein subcellular location, only covers five location sites. Also, the data set used to train PSORT-B contains many proteins with high degrees of sequence identity in a same location group and, hence, may bear a strong homology bias. To overcome these problems, a new predictor, called "Gneg-PLoc", is developed. Featured by fusing many basic classifiers each being trained with a stringent data set containing proteins with strictly less than 25% sequence identity to one another in a same location group, the new predictor can cover eight subcellular locations; that is, cytoplasm, extracellular space, fimbrium, flagellum, inner membrane, nucleoid, outer membrane, and periplasm. In comparison with PSORT-B, the new predictor not only covers more subcellular locations, but also yields remarkably higher success rates. Gneg-PLoc is available as a Web server at http://202.120.37.186/bioinf/Gneg. To support the demand of people working in the relevant areas, a downloadable file is provided at the same Web site to list the results identified by Gneg-PLoc for 49 907 Gram-negative protein entries in the Swiss-Prot database that have no subcellular location annotations or are annotated with uncertain terms. The large-scale results will be updated twice a year to cover the new entries of Gram-negative bacterial proteins and reflect the new development of Gneg-PLoc.
authors.affiliation: Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, California 92130, USA. kchou@san.rr.com
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism*; D030541:Databases, Genetic; D006090:Gram-Negative Bacteria; D020407:Internet; D040901:Proteomics; D012984:Software; D013347:Subcellular Fractions / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D001426:Bacterial Proteins