Inferring domain-domain interactions using an extended parsimony model

High-throughput technologies have produced a large number of protein-protein interactions (PPIs) for different species. As protein domains are functional and structural units of proteins, many computational efforts have been made to identify domain-domain interactions (DDIs) from PPIs. Parsimony assumption is widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they could detect specific DDIs, which is often hard for many probabilistic methods. In this paper, we revisit the parsimony model by presenting two important extensions. First, ‘complex networks’ as an emerging concept is incorporated as prior knowledge into the parsimony model. With this improvement, the prediction accuracy increases, which to some extent enhances the biological meaning of the common property of complex networks. Second, two randomization tests are designed to show the parsimony nature of the DDIs in mediating PPIs, which corroborates the model validation.