Predicting subcellular localization of proteins in a hybridization space.
BACKGROUND
The localization of a protein in a cell is closely correlated with its biological function. With the number of sequences entering into databanks rapidly increasing, the importance of developing a powerful high-throughput tool to determine protein subcellular location has become self-evident. In view of this, the Nearest Neighbour Algorithm was developed for predicting the protein subcellular location using the strategy of hybridizing the information derived from the recent development in gene ontology with that from the functional domain composition as well as the pseudo amino acid composition.


RESULTS
As a showcase, the same plant and non-plant protein datasets as investigated by the previous investigators were used for demonstration. The overall success rate of the jackknife test for the plant protein dataset was 86%, and that for the non-plant protein dataset 91.2%. These are the highest success rates achieved so far for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach (particularly by incorporating the knowledge of gene ontology) may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology.


BACKGROUND
The software would be made available on sending a request to the authors.
Cai
Yu-Dong
YD
Biomolecular Sciences Department, UMIST, PO Box 88, Manchester M60 1QD, UK.
Chou
Kuo-Chen
KC
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism*; D014018:Tissue Distribution
D011506:Proteins