ArticleMolecular & cellular proteomics : MCP2013
Predicting physical interactions between protein complexes.
Article in Molecular & cellular proteomics : MCP, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Graph theory and stability analysis of protein complex interaction networks.IET systems biology · 2016Article
- Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.BioMed research international · 2015Article
- Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes.PloS one · 2015Article
- Differential protein network analysis of the immune cell lineage.BioMed research international · 2014Article
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Authors and funding
4 authors.
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Abstract
Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.
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