Abstract
BackgroundThe identification and characterization of interacting domain pairs is an important step towards understanding protein interactions. In the last few years, several methods to predict domain interactions have been proposed. Understanding the power and the limitations of these methods is key to the development of improved approaches and better understanding of the nature of these interactions.ResultsBuilding on the previously published Parsimonious Explanation method (PE) to predict domain-domain interactions, we introduced a new Generalized Parsimonious Explanation (GPE) method, which (i) adjusts the granularity of the domain definition to the granularity of the input data set and (ii) permits domain interactions to have different costs. This allowed for preferential selection of the so-called "co-occurring domains" as possible mediators of interactions between proteins. The performance of both variants of the parsimony method are competitive to the performance of the top algorithms for this problem even though parsimony methods use less information than some of the other methods. We also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the GPE method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset.ConclusionThe Generalized Parsimonious Explanation approach provides a new means to predict and study domain-domain interactions. We showed that, under the assumption that all protein interactions in the network are mediated by domain interactions, there exists a significant deviation of the properties of domain interactions mediating interactions in the network from that of iPfam data.
Highlights
The identification and characterization of interacting domain pairs is an important step towards understanding protein interactions
Protein interaction data is collected from studies of individual systems, and more recently through high-throughput experiments, such as yeast two-hybrid (Y2H) and tandem affinity purification followed by mass spectrometry (TAP/MS) [4,5,6,7,8,9,10,11]
Just as in the general case of evolution, where some changes are more likely than others, some types of domain interactions may be preferred to others for biological reasons. To model this possibility we introduce a new variant of the parsimony approach, Generalized Parsimonious Explanation (GPE), which allows for a differential treatment of different types of domain pairs
Summary
The identification and characterization of interacting domain pairs is an important step towards understanding protein interactions. Protein interaction data is collected from studies of individual systems, and more recently through high-throughput experiments, such as yeast two-hybrid (Y2H) and tandem affinity purification followed by mass spectrometry (TAP/MS) [4,5,6,7,8,9,10,11] Those methods provide a vast amount of interaction data, but several independent studies indicate false positive rates of the order of 50% [1215]. Deng and colleagues [21] proposed an expectation maximization algorithm (EM) which computes domain interaction probabilities that maximize the expectation of observing a given proteinprotein interaction network Other approaches to this problem use linear programming [22], support vector machines [23], probabilistic network modeling [24], and lowest p-value [25]
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