title: Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach.
abstract: The location of a protein in a cell is closely correlated with its biological function. Based on the concept that the protein subcellular location is mainly determined by its amino acid and pseudo amino acid composition (PseAA), a new algorithm of increment of diversity combined with support vector machine is proposed to predict the protein subcellular location. The subcellular locations of plant and non-plant proteins are investigated by our method. The overall prediction accuracies in jackknife test are 88.3% for the eukaryotic plant proteins and 92.4% for the eukaryotic non-plant proteins, respectively. In order to estimate the effect of the sequence identity on predictive result, the proteins with sequence identity <or=40% are selected. The overall success rates of prediction are 86.2% and 92.3% for plant and non-plant proteins in jackknife test, respectively.
authors.affiliation: Laboratory of Theoretical Biophysics, Department of Physics, College of Sciences and Technology, Inner Mongolia University, Hohhot 010021, China.
authors.forename: Feng-Min
authors.initials: FM
authors.lastname: Li
authors.forename: Qian-Zhong
authors.initials: QZ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005057:Eukaryotic Cells / Q000166:cytology / Q000378:metabolism; D042541:Intracellular Space / Q000378:metabolism*; D059828:Plant Cells; D010940:Plant Proteins / Q000737:chemistry / Q000378:metabolism; D010944:Plants / Q000378:metabolism; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D020539:Sequence Analysis, Protein
chemical_list: D010940:Plant Proteins; D011506:Proteins