Using discriminant function for prediction of subcellular location of prokaryotic proteins.
The discriminant function algorithm was introduced to predict the subcellular location of proteins in prokaryotic organisms from their amino-acid composition. The rate of correct prediction for the three possible subcellular locations of prokaryotic proteins studied by Reinhardt and Hubbard (Nucleic Acid Research, 1998, 26:2230-2236) was 90% by the self-consistency test, and 87% by the jackknife test. These rates are considerably higher than the results recently reported by them using the neural network method. Furthermore, the test procedure adopted here is also more rigorous. The core of the current algorithm is the covariance matrix, through which the collective interactions among different amino-acid components of a protein can be reflected. It is anticipated that, owing to the intimate correlation of the function of a protein with its subcellular location, the current algorithm will become a useful tool for the systematic analysis of genome data.
Chou
K C
KC
Computer-Aided Drug Discovery, Pharmacia & Upjohn, Kalamazoo, Michigan, 49007-4940, USA.
Elrod
D W
DW
D000465:Algorithms; D003593:Cytoplasm / Q000737:chemistry; D019992:Databases as Topic; D016002:Discriminant Analysis; D005110:Extracellular Space / Q000737:chemistry; D016678:Genome; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011387:Prokaryotic Cells / Q000737:chemistry / Q000648:ultrastructure*; D011506:Proteins / Q000032:analysis* / Q000737:chemistry / Q000235:genetics; D015203:Reproducibility of Results; D013347:Subcellular Fractions / Q000737:chemistry* / Q000648:ultrastructure
D011506:Proteins