Edge-count probabilities for the identification of local protein communities and their organization.
We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.
Farutin
Victor
V
Computational Sciences, Informatics, Millennium Pharmaceuticals Inc., Cambridge, Massachusetts 02139, USA. farutin@mpi.com
Robison
Keith
K
Lightcap
Eric
E
Dancik
Vlado
V
Ruttenberg
Alan
A
Letovsky
Stanley
S
Pradines
Joel
J
D002448:Cell Adhesion; D016764:Cell Polarity; D006801:Humans; D008958:Models, Molecular; D009415:Nerve Net; D011336:Probability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000502:physiology; D011956:Receptors, Cell Surface / Q000737:chemistry / Q000502:physiology; D017411:Ribonucleoproteins, Small Nuclear / Q000737:chemistry; D015398:Signal Transduction
D011506:Proteins; D011956:Receptors, Cell Surface; D017411:Ribonucleoproteins, Small Nuclear