With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

• PMID: 15706503
• PubMed
• PubTator

Reference Type

Journal Article | Research Support, Non-U.S. Gov't | Review

Authors

Han DS, Kim HS, Jang WH, Lee SD, Suh JK

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