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. 2006 May 25:7:269.
doi: 10.1186/1471-2105-7-269.

An integrated approach to the prediction of domain-domain interactions

Hyunju Lee  1 , Minghua Deng, Fengzhu Sun, Ting Chen
Affiliations

An integrated approach to the prediction of domain-domain interactions

Hyunju Lee et al. BMC Bioinformatics. .

Abstract

Background: The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.

Results: We combine protein interaction data sets from multiple species, molecular sequences, and gene ontology to construct a set of high-confidence domain-domain interactions. First, we propose a new measure, the expected number of interactions for each pair of domains, to score domain interactions based on protein interaction data in one species and show that it has similar performance as the E-value defined by Riley et al. Our new measure is applied to the protein interaction data sets from yeast, worm, fruitfly and humans. Second, information on pairs of domains that coexist in known proteins and on pairs of domains with the same gene ontology function annotations are incorporated to construct a high-confidence set of domain-domain interactions using a Bayesian approach. Finally, we evaluate the set of domain-domain interactions by comparing predicted domain interactions with those defined in iPfam database that were derived based on protein structures. The accuracy of predicted domain interactions are also confirmed by comparing with experimentally obtained domain interactions from H. pylori. As a result, a total of 2,391 high-confidence domain interactions are obtained and these domain interactions are used to unravel detailed protein and domain interactions in several protein complexes.

Conclusion: Our study shows that integration of multiple biological data sets based on the Bayesian approach provides a reliable framework to predict domain interactions. By integrating multiple data sources, the coverage and accuracy of predicted domain interactions can be significantly increased.

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Figures

Figure 1
Figure 1
A Venn diagram for the numbers of domains in yeast, worm, fruitfly, and humans. (a) The numbers of domains in yeast, worm, and fruitfly. (b) The numbers of domains between humans and the other three species.
Figure 2
Figure 2
A Venn diagram for the numbers of predicted domain-domain interactions in yeast, worm, fruitfly, and humans. (a) The numbers of predicted domain-domain interactions in yeast, worm, and fruitfly. (b) The numbers of predicted domain-domain interactions between humans and the other three species.
Figure 3
Figure 3
The relationship between rank and true positive rate (TP/(TP+FP)) compared to the iPfam for four species based on four score functions. "Expectation" ranks domain pairs according to the expected number of occurrences of domain pairs in protein interactions; "Probability" ranks domain pairs according to the estimated probability of interactions from the MLE method; "Frequency" ranks domain pairs according to the number of protein interactions having domain pair; "E-value" ranks domain pairs according to the E-value defined in [1].
Figure 4
Figure 4
The relationship between false positive rate and sensitivity for predicting domain interactions using the Bayesian method with different data sources. The letters Y, W, F, H, C, and G indicate domain interactions based on yeast, worm, fruitfly, humans, co-existence, and same GO function, respectively. YWFH.Liu shows the result of predicted domain interactions using the extended MLE method defined in Liu et al. [14] with protein interactions of yeast, worm, fruitfly, and humans.
Figure 5
Figure 5
The relationship between false positive rate and sensitivity for predicting domain interactions using different methods : evidence counting, logistic regression, and naive Bayesian.
Figure 6
Figure 6
Two examples of yeast complexes with predicted domain-domain interactions and MIPS physical protein interactions. The black arrows are predicted DDIs, the grey arrows are DDIs in iPfam, and the red arrows are PPIs from DIP. (a) SCF (Skp1-Cdc53-F-box protein) complexes. Cdc53 controls G1/S transition. Cdc34 is E2 ubiquitin-conjugating enzyme. Skp1 is kinetochore protein complex Cbf3, subunit D. Cdc4, Met30, and Grr1 are the F-box proteins. (b) Pyruvate dehydrogenase complexes. Pdb1 is pyruvate dehydrogenase (lipoamide) beta chain precursor, Pda1 is pyruvate dehydrogenase (lipoamide) alpha chain precursor, Lpd1 is dihydrolipoamide dehydrogenase precursor, Pdx1 is pyruvate dehydrogenase complex protein X, and Lat1 is dihydrolipoamide S-acetyltransferase. For details, see the main text.
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