A new computational approach to analyze human protein complexes and predict novel protein interactions.
We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated alpha-tubulin and early endosome antigen 1 as its novel interactors.
Department of Oncological Sciences and Division of Molecular Angiogenesis, Institute for Cancer Research and Treatment (IRCC), University of Torino Medical School, Strada Provinciale, I-10060 Candiolo (Turin), Italy. zanivan@biochem.mpg.de
Sara
S
Zanivan
Ilaria
I
Cascone
Chiara
C
Peyron
Ivan
I
Molineris
Serena
S
Marchio
Michele
M
Caselle
Federico
F
Bussolino
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005786:Gene Expression Regulation; D006367:HeLa Cells; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D011506:Proteins / Q000378:metabolism*; D012680:Sensitivity and Specificity; D012984:Software; D054462:p21-Activated Kinases / Q000235:genetics / Q000378:metabolism
D011506:Proteins; C515903:PAK1 protein, human; D054462:p21-Activated Kinases