Abstract
BackgroundAs protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. In deterministic methods, parsimony assumption has been utilized. Parsimony principle has been 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 can detect specific DDIs, which is often hard for many probabilistic methods. We notice that existing methods just view PPI networks as simply assembled by single interactions, but there is now ample evidence that PPI networks should be considered in a global (systematic) point of view for it exhibits general properties of complex networks, such as 'scale-free' and 'small-world'.ResultsIn this work, we integrate this global point of view into the parsimony-based model. Particularly, prior knowledge is extracted from these global properties by plausible reasoning and then taken as input. We investigate the role of the added information extensively through numerical experiments. Results show that the proposed method has improved performance, which confirms the biological meanings of the extracted prior knowledge.ConclusionsThis work provides us some clues for using these properties of complex networks in computational models and to some extent reveals the biological meanings underlying these general network properties.
Highlights
As protein domains are functional and structural units of proteins, a large proportion of proteinprotein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental protein-protein interaction (PPI) since high throughput technologies have produced a large number of PPIs for different species
These interactions can be represented as a protein-protein interaction (PPI) network with each node corresponding to a protein and each edge an interaction
While DDIs are difficult to be determined experimentally, many computational approaches have been proposed aiming at discovering the patterns from DDIs, among which parsimony-based models show their advantages in easy implementation and power in detecting specific DDIs
Summary
As protein domains are functional and structural units of proteins, a large proportion of proteinprotein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. Researchers have confirmed that most proteins perform their functions through physically binding to other proteins, permanently or transiently These interactions can be represented as a protein-protein interaction (PPI) network with each node corresponding to a protein and each edge an interaction. Since interactions between domains are difficult to be determined experimentally, many computational approaches have been proposed to discover DDI patterns from experimental PPIs
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