These authors contributed equally to this work.
Identification of additional proteins in differential proteomics using protein interaction networks
Article first published online: 5 APR 2013
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Volume 13, Issue 7, pages 1065–1076, April 2013
How to Cite
Gwinner, F., Acosta-Martin, A. E., Boytard, L., Chwastyniak, M., Beseme, O., Drobecq, H., Duban-Deweer, S., Juthier, F., Jude, B., Amouyel, P., Pinet, F. and Schwikowski, B. (2013), Identification of additional proteins in differential proteomics using protein interaction networks. Proteomics, 13: 1065–1076. doi: 10.1002/pmic.201200482
Colour Online: See the article online to view Figs. 1–4 in colour.
- Issue published online: 5 APR 2013
- Article first published online: 5 APR 2013
- Accepted manuscript online: 5 FEB 2013 11:57AM EST
- Manuscript Accepted: 7 JAN 2013
- Manuscript Revised: 22 DEC 2012
- Manuscript Received: 22 OCT 2012
- “Fondation pour la Recherche Médicale,” a European FAD. Grant Number: Health-F2-2008-200647
- NIH. Grant Number: P41 GM103504
- Data analysis;
- Protein–protein interactions;
- Smooth muscle cells;
- Steiner tree
In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest-ranked proteins, beta-arrestin 1, and beta-arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost-effective means to identify additional proteins that remain elusive for current 2D gel-based proteomic profiling techniques.