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.
Highlights
From the ‡Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital and Oslo University Hospital, Oslo, the ¶Biomedical Research Group, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, the Center for Cancer Biomedicine, University of Oslo, Oslo, the **Bioinformatics Core Facility, Institute of Medical Informatics, University of Oslo, Oslo University Hospital, Oslo, and the ‡‡Institute of Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
We integrated only binary physical protein interactions that were experimentally verified and supported by Medline references, from the iRefIndex database [24, 25], and we developed a statistical method that compared the number of observed physical protein interactions between pairs of protein complexes versus the number of protein interactions expected to be present in pairs of randomized protein complexes
For each pair of manually curated protein complexes, we counted the number of physical protein interactions between their proteins, which we refer to as the complex-complex degree
Summary
Guided by a computational method to predict the list of protein members in the complexes [10], this allowed a screen of putative intercomplex relationships from human cell lines [7] This adds to the many landmark developments in recent years to characterize protein complexes in a genome-wide manner [7, 11,12,13]. This noise would in turn cause ambiguity when attempting to predict, genome-wide, interactions that may occur between protein complexes One solution to this problem, as applied in this study, is the use of comprehensive databases of the so-called “gold standard” community definitions of protein complexes (19 –22). Such higher order perspectives of cellular proteomes may aid discovery of novel functional relationships and lead to an improved understanding of cellular behavior
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