Computational analysis of protein-protein interactions in metabolic networks of Escherichia coli and yeast

Protein-protein interactions are operative at almost every level of cell function. In the recent years high-throughput methods have been increasingly used to uncover protein-protein interactions at genome scale resulting in interaction maps for entire organisms. However, biochemical implications of high-throughput interactions are not always obvious. The question arises whether all interactions detected by in vitro experiments also play a functional role in the living cell. In this work we systematically analyze high-throughput protein-protein interactions stored in public databases in the context of metabolic networks. Classifying reaction pairs according to their topological distance revealed a significantly higher frequency of enzyme-enzyme interactions for directly neighbored reactions (distance = 1). To determine possible functional implications for these interactions we examined randomized networks using original enzyme interactions as well as randomly generated interaction data. A functional relevance of enzyme-enzyme interactions could be demonstrated for those reactions that exhibit low connectivity. As this is a characteristic of enzyme pairs in metabolic channeling we systematically searched the literature and indeed recovered a certain fraction of enzyme pairs that has already been implicated in metabolic channeling. However, a substantial number of enzyme pairs uncovered by our large-scale analysis remains that up to now has neither been functionally nor structurally classified and therefore present novel candidates of the metabolic channeling concept.