Open Access Highly Accessed Open Badges Method

A new computational approach to analyze human protein complexes and predict novel protein interactions

Sara Zanivan12*, Ilaria Cascone13, Chiara Peyron4, Ivan Molineris4, Serena Marchio1, Michele Caselle4 and Federico Bussolino1

Author Affiliations

1 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

2 Max-Planck Institute for Biochemistry, Department of Proteomics and Signal Transduction, Am. Klopferspitz, D-82152 Martinsried, Germany

3 Inserm U528, Institut Curie, 75248 Paris, France

4 Department of Theoretical Physics, University of Torino and INFN, Via P Giuria 1, I-10125 Turin, Italy

For all author emails, please log on.

Genome Biology 2007, 8:R256  doi:10.1186/gb-2007-8-12-r256

Published: 4 December 2007


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 α-tubulin and early endosome antigen 1 as its novel interactors.