title: Predicting domain-domain interactions using a parsimony approach.
abstract: We propose a novel approach to predict domain-domain interactions from a protein-protein interaction network. In our method we apply a parsimony-driven explanation of the network, where the domain interactions are inferred using linear programming optimization, and false positives in the protein network are handled by a probabilistic construction. This method outperforms previous approaches by a considerable margin. The results indicate that the parsimony principle provides a correct approach for detecting domain-domain contacts.
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
authors.forename: Katia S
authors.initials: KS
authors.lastname: Guimarães
authors.forename: Raja
authors.initials: R
authors.lastname: Jothi
authors.forename: Elena
authors.initials: E
authors.lastname: Zotenko
authors.forename: Teresa M
authors.initials: TM
authors.lastname: Przytycka
mesh_terms: D030562:Databases, Protein; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011485:Protein Binding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D050600:SNARE Proteins / Q000378:metabolism; D018631:ras Proteins / Q000378:metabolism
chemical_list: D011506:Proteins; D050600:SNARE Proteins; D018631:ras Proteins