Predicting subcellular localization of proteins using machine-learned classifiers.
BACKGROUND
Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location.


RESULTS
We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.
Department of Computing Science, University of Alberta, Edmonton, AB, Canada, T6G 2E8.
Z
Z
Lu
D
D
Szafron
R
R
Greiner
P
P
Lu
D S
DS
Wishart
B
B
Poulin
J
J
Anvik
C
C
Macdonell
R
R
Eisner
D000465:Algorithms; D001185:Artificial Intelligence; D022082:Cellular Structures / Q000378:metabolism*; D016000:Cluster Analysis; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism*; D020543:Proteome / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software; D014018:Tissue Distribution; D014584:User-Computer Interface
D011506:Proteins; D020543:Proteome