Published online 1 December 2004
Nucleic Acids Research, Vol. 32 No. 21 © Oxford University Press 2004; all rights reserved
PreSPI: a domain combination based prediction system for protein–protein interaction
School of Engineering, Information and Communications University, 119, Munjiro, Yuseong-gu, Daejeon 305-714, Korea and 1 Proteomics and Bioinformatics Program, LG Life Sciences R&D, 104-1, Munji-dong, Yuseong-gu, Daejeon 305-380, Korea
* To whom correspondence should be addressed. Tel: +82 42 866 6130; Fax: +82 42 866 6222; Email: dshan{at}icu.ac.kr
The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors
Received August 5, 2004; Revised October 20, 2004; Accepted November 10, 2004
With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.
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