title: Enhanced functional information from predicted protein networks.
abstract: Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.
authors.affiliation: Computational Genomics Group, Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: McDermott
authors.forename: Ram
authors.initials: R
authors.lastname: Samudrala
mesh_terms: D004734:Energy Metabolism / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000502:physiology*; D040901:Proteomics / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D020543:Proteome