Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species.
Nucleic Acids Res
; 36(20): e136, 2008 Nov.
Article
em En
| MEDLINE
| ID: mdl-18836191
ABSTRACT
The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a protein's extended protein-protein interaction neighborhood, using a classification framework called 'Divide and Conquer k-Nearest Neighbors' (DC-kNN). These predictions achieve significantly higher accuracy than two well-known methods for predicting protein localization in yeast. Using new GFP imaging experiments, we show that the network-based approach can extend and revise previous annotations made from high-throughput studies. Finally, we show that our approach remains highly predictive in higher eukaryotes such as fly and human, in which most localizations are unknown and the protein network coverage is less substantial.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Mapeamento de Interação de Proteínas
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos