Abstract
BackgroundWhile the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well.ResultsWe consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly.Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation.ConclusionOur results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.
Highlights
While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are determined by their static grid of links
Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities
We show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions
Summary
While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are determined by their static grid of links. The most dramatic is the scalefree nature of these networks, a remarkable inhomogeneity that highlights a small number of highly connected nodes which secure the networks integrity [1] The special role such proteins play for the stability of protein interaction networks is further indicated by their significant propensity to be simultaneously essential as well as evolutionary conserved [2]. Cohesively bound motifs of protein networks are frequently conserved as a whole, suggesting their role as evolutionary relevant units [5] While these findings allowed spectacular insights into the inner workings of a cell, biological networks are generally determined by their layout of links. In contrast to other real world networks, we find weak signals that do not support an entanglement of static grid and weights of domain interactions, allowing us to confirm that a protein interactions are largely governed by single domain interactions
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