Abstract
BackgroundDeciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.MethodWe integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.ResultIDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.
Highlights
Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins
To estimate the reliability of predicted domain-domain interaction (DDI), we developed a novel scoring scheme considering the individual accuracy of each datasets, independency among the datasets and the internal prediction scores of the DDIs measured by each method
Among DDIs, 6,768 interactions were combined from 3D structure-based datasets and 202,914 interactions were extracted from predicted datasets
Summary
Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Protein interactions, including binary PPIs and co-complexes, regulate biological process and biochemical reactions. Discovering protein interactions provides detailed interpretation of cellular mechanism of biological functions. The identification of protein interaction is a critical issue for biology researchers. Massive amount of protein interaction data is available due to the advancement of large-scale screening techniques. Investigating protein interactions in domain level can complement these limitations. Domain-domain interactions (DDIs) are crucial clues of protein interactions. DDIs can be key supporting evidences for protein interaction mechanisms
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