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

doi:10.1186/gb-2007-8-12-r256

Genome Biology

Volume 8
Issue 12

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Zanivan S
Cascone I
Peyron C
Molineris I
Marchio S
Caselle M
Bussolino F

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Cascone I
Peyron C
Molineris I
Marchio S
Caselle M
Bussolino F
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A new computational approach to analyze human protein complexes and predict novel protein interactions

Sara Zanivan¹^,2 , Ilaria Cascone¹^,3 , Chiara Peyron⁴ , Ivan Molineris⁴ , Serena Marchio¹ , Michele Caselle⁴^* and Federico Bussolino¹^*

¹Department 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

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

³Inserm U528, Institut Curie, 75248 Paris, France

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

author email corresponding author email* Contributed equally

Genome Biology 2007, 8:R256doi:10.1186/gb-2007-8-12-r256


Published:	4 December 2007

Subject areas: Bioinformatics, Genome studies

Abstract

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.