title: Predicting 22 protein localizations in budding yeast.
abstract: According to the recent experiments, proteins in budding yeast can be distinctly classified into 22 subcellular locations. Of these proteins, some bear the multi-locational feature, i.e., occur in more than one location. However, so far all the existing methods in predicting protein subcellular location were developed to deal with only the mono-locational case where a query protein is assumed to belong to one, and only one, subcellular location. To stimulate the development of subcellular location prediction, an augmentation procedure is formulated that will enable the existing methods to tackle the multi-locational problem as well. It has been observed thru a jackknife cross-validation test that the success rate obtained by the augmented GO-FnD-PseAA algorithm [BBRC 320 (2004) 1236] is overwhelmingly higher than those by the other augmented methods. It is anticipated that the augmented GO-FunD-PseAA predictor will become a very useful tool in predicting protein subcellular localization for both basic research and practical application.
authors.affiliation: Biomolecular Sciences Department, UMIST, P.O. Box 88, Manchester M60 1QD, UK; Gordon Life Science Institute, San Diego, CA 92130, USA. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D005656:Fungal Proteins / Q000737:chemistry* / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D004718:Saccharomycetales / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013347:Subcellular Fractions / Q000378:metabolism*; D014018:Tissue Distribution
chemical_list: D005656:Fungal Proteins